FedClean: A General Robust Label Noise Correction for Federated Learning
Authors: Xiaoqian Jiang
Jing Zhang
Venue: ICML 2025 poster (2025)
Keywords: federated learning
noisy labels
model aggregation
Primary area: general_machine_learning

Abstract: Many federated learning scenarios encounter label noises in the client-side datasets. The resulting degradation in global model performance raises the urgent need to address label noise. This paper proposes FedClean -- a novel general robust label noise correction for federated learning. FedClean first uses the local centralized noisy label learning to select clean samples to train a global model. Then, it employs a two-stage correction scheme to correct the noisy labels from two distinct perspectives of local noisy label learning and the global model. FedClean also proposes a novel model aggregation method, further reducing the impact of label noises. FedClean neither assumes the existence of clean clients nor the specific noise distributions, showing the maximum versatility. Extensive experimental results show that FedClean effectively identifies and rectifies label noises even if all clients exhibit label noises,  which outperforms the state-of-the-art noise-label learning methods for federated learning.

FedClean: A General Robust Label Noise Correction for Federated Learning
Xiaoqian Jiang' Jing Zhang
Abstract
(Fang & Ye, 2022) becomes a critical challenge in feder- ated learning, which significantly degrades the performance of global models (Zeng et al., 2023). Therefore, federated learning still has an urgent need to deal with label noise.
Many federated learning scenarios encounter la- bel noises in the client-side datasets. The resulting degradation in global model performance raises the urgent need to address label noise. This pa- per proposes FedClean - novel general robust label noise correction for federated learning. Fed- Clean first uses the local centralized noisy label learning to select clean samples train a global model. Then, it employs a two-stage correction scheme to correct the noisy labels from two dis- tinct perspectives of local noisy label learning and the global model. FedClean also proposes a novel model aggregation method, further reduc- ing the impact of label noises. FedClean neither assumes the existence of clean clients nor the spe- cific noise distributions, showing the maximum versatility. Extensive experimental results show that FedClean effectively identifies and rectifies label noises even if all clients exhibit label noises, which outperforms the state-of sate-of-theear the-art noise-label learning methods for federated learning.
Label noise issue has been extensively studied within the context of centralized learning (CL), leading to the devel- opment of numerous advanced methods (Song et al., 2022). However, these CL--based methods cannot be directly ap- plied to FL due to its inherent privacy constraints, which prohibit sharing data between clients with global server. As a result, the restricted size and insufficient diversity of the local datasets in clients dramatically degrade the effec- tiveness of noise processing methods. For example, Xu et al. (2022) have demonstrated that even the most sophisticated label noise correction methods for CL (Li et al., 2020a; Tanaka et al., 2018), when applied client datasets, insufficient to alleviate performance degradation in FL..
Therefore, recent studies have begun to address the label noise issue within the FL framework. Some methods at- tempt to mitigate label noise by discarding (Xu & Lyu, 2020) re-weighting (Wan & Chen, 2021; Fu et al., 2021; Chen et al., 2020) the model updates in the clients that are least similar to those in other clients. They treat the dis- carded or re-weighted clients as malicious ones, which is obviously too radical. Many clients may simply have some label noises in their local datasets, which, with proper cor- rection, could still contribute valuable information to the global model. To this end, latest methods were proposed to distinguish between clean (where all labels of local train- ing data are correct) and noisy clients and then use models trained from clean clients to correct noisy labels in noisy clients (Xu et al., 2022; Wu et al.. 2023; Jiang et al., 2024).
1. Introduction
Federated learning (FL) has emerged as a powerful frame- work for decentralized machine learning (Liu et al., 2022), enabling multiple clients to collaboratively train models without sharing raw data, thus preserving privacy (Wen et al., 2023; Huang et al., 2023). This learning paradigm is especially valuable in privacy-sensitive domains (Nevrataki et al.. 2023) such as healthcare (Rani et al., 2023; Thum- misetti & Atluri, 2024), finance (Li & Wen, 2023; Awosika et al., 2024), and IoT (Yadav et al., 2022: Rjoub et al., 2024), where data privacy regulations like GDPR (Zaeem & Bar- ber, 2020) are imposed protect user information strictly. Since there is no centralized entity to preprocess the whole training data, the presence of label noise in client datasets
However, all existing label noise correction methods (Xu et al., 2022: Wu et al., 2023; Jiang et al., 2024) for FL are based on an idealized assumption that some clean clients exist, which unfortunately does not always hold in real- ity. For example, federated crowdsoureing learning (Guo et al., 2020) recruits non-expert workers from the Internet to collect data and train local models, where each client is probably imperfect. In other scenarios when clients are maliciously attacked, data are contaminated, or devices are compromised (Sharma & Marchang, 2024), the assump- tion also breaks up. Moreover, these methods distinguish
Schooo of Cyber Science and Engineering, Southeast Univer- sity, No.2 SEU Road, Nanjing 211189, China. Correspondence to: Jing Zhang <jingz@ scu.edu.cn>.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
between clean and noisy clients for different processing, potentially exposing client privacy. For instance, an attacker can infer from the communication patterns between clients and the server whether client is noisy or clean (Bai et al., 2024). However, in real-world FI. scenarios, clients are usu- ally unwilling to disclose whether their data is noisy, as it may lower their reputation, or clean, as it may expose them to malicious attacks. What's more, some further assume specific noise distributions (class-conditional noise (Ji et al., 2024; Wu et al., 2023), instance-dependent noise (Wang et al., 2023)), further narrowing their application scope.
ods like Co-teaching (Han et al., 2018) and Co-teaching+ (Yu et al., 2019) use two peer networks to exchange sam- ples, with those having lower loss values assumed to be more reliable. Robust loss functions such as Symmetric CE (Wang et al., 2019) integrate model predictions into the loss function, while Joint Optim (Tanaka et al., 2018) refines labels by averaging model predictions over epochs. SELFIE (Song et al., 2019) focuses on robustly identify- ing potential noisy samples and gradually incorporating them into the training process. DivideMix (Li et al., 2020a) combines Co-teaching, MixUp (Zhang et al., 2018), and MixMatch (Berthelot et al., 2019) for more robust noise handling. These CNLL methods can be adapted to FL by in- tegrating with standard aggregation techniques like FedAvg (McMahan et al., 2016).
To address the challenges outlined above, we propose Fed- Clean, a
robust noise correction frame- work for FI.. FedClean first uses the local centralized noisy label learning (CNLL) to select clean samples for global model training. Then, its two-stage correction scheme uses the information obtained from the local CNLL and the global model to correct the noisy labels. FedClean also proposes a novel adaptive sample size-weighted aggrega- tion method (ASSA) to reduce the impact of label noise and further improve the performance of the global model. FedClean neither assumes the existence of clean clients nor the specific noise distributions. As such, FedClean shows outstanding robustness when the ratio of noisy clients is very high. Even if all clients are noisy, FedClean also performs well. The contributions of this study are three-fold:
2.2. Federated Noisy Label Learning (FNIL)
Label noise in FL has led to several strategies, primarily focusing on reweightin//diseardnn noisy data, leveraging clean datasets, and distinguishing between clean and noisy clients: i) Reweighting and discarding noisy data. Meth- ods like Client Confidence Reweighting (CCR) (Fang & Ye, 2022) assign adaptive weights to clients based on data quality. FedNoiL (Wang et al., 2022) selects clients with fewer noisy labels and discards noisy samples, while RFFL (Xu & Lyu, 2020) uses reputation- based client evaluation to exclude unreliable clients. These methods may discard valuable data and fail fully leverage noisy samples. ii) Leveraging additional clean datasets. Techniques like FO- CUS (Chen et al., 2020) use benchmark datasets on the server side to assess client data credibility and adjust client weights accordingly. Client selection algorithms (Yang et al., 2021) identify clients with low noise using clean validation datasets. However, these methods rely on the availability of clean datasets, which may always be feasible. iii) Distin- guishing between clean and noisy clients. More advanced methods dynamically distinguish between clean and noisy clients. FedCorr (Xu et al., 2022) uses model prediction sub- spaces to identify noisy clients, while FedNoRo (Wu et al., 2023) applies Gaussian mixture models and knowledge dis- tillation for more robust aggregation. FedELC (Jiang et al., 2024) detects noisy clients and corrects their labels via back- propagation. However, these methods assume the existence of clean clients and struggle when such clients are absent or noisy labels are pervasive.
1) We propose a novel general robust label noise correction for federated learning, where a two-stage label correction scheme is proposed to identify and rectify noisy labels from two distinet perspectives local noisy label learn- ing and global FL models. The framework also intro- duces novel collaborative per-sample loss to assess the confidence in label corrections, which helps reduce the likelihood of false correction.
2) We propose a novel adaptive sample size-weighted aggre- gation (ASSA) method for FL. that adjusts client influence based on clean sample sizes to reduce noisy label impact, while incorporating zkCor (Wang et al., 2024) for secure label correction.
3) Extensive experiments on datasets with synthetic label noises and a real-world dataset consistently demonstrate that the proposed FedClean effectively identifies and rec- tifies label noises hence mitigating the performance degra- dation of FL models, even all the clients are affected by label noises, which outperforms the state-of- oate-offthhe-an noise-label learning methods for FL.
Other approaches to FNLL include Robust FL (Yang et al., 2022), which aligns client data via class-wise centroids, and FedLSR (Jiang et al., 2022), which employs self-distillation for local regularization to enhance privacy. FedRN (Kim et al., 2022) increases communication overhead by main- taining reliable neighbor models, while FedFixer (Ji et al., 2024) uses personalized models to mitigate the impact of noisy labels but is dependent on specific noise distributions.
2. Related Work
2.1. Centralized Noisy Label Learning (CNLL)
In centralized learning, noisy label correction has been ex- plored through various techniques. Sample selection meth-
2

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Server
FedClean on Client k
Client/ I
Client 2
Server
7
Dr
D
CNLL
2
D
Client N
Client
D
De
Downlunding
Local
D2
DD
Sub-stage
Sub-stage
Preprocessing stage
Label noise correction stage
ASSA (propased)
Figure 1. Framework of FedClean. Algorithm steps numbered accordingly.
3. The Proposed Method
3.2. Framework of FedClean
3.1. Preliminaries
We propose FedClean, a general robust label noise correc- tion method for FL. The framework of FedClean is shown in Figure 1, which has two key stages. In the preprocess- ing stage, FedClean enables each client to perform local CNLL, generating inferred labels for each sample and ini- tially screening clean samples to train a global model. Dur- ing the label noise correction stage, we propose a two-stage correction scheme that integrates inferred labeling with the global model to accurately identify and correct noisy la- bels. Within this inferred label-based correction process, we define a collaborative per-sample loss function eval- uate the confidence of the label corrections. To enhance the robustness of federated training against label noise, we incorporate the Mixup and the proposed Adaptive Sam- ple weighte Aggregation (ASSA). Notably, FedClean does not distinguish between "clean" and "noisy" clients, ef- fectively correcting label noise in client data while adhering to the privacy constraints of FL.
We consider a federated learning system consisting of N clients N = {1....,NY N) and their local dataset {D& N where D; ((rr).z))) denotes the local dataset for client & E NN, vi represents the annotation label for the sample zf.- the conclusion of the f-round communication, wt is defined as the local model weight of client k, while w² represents the weight of the aggregated global model %. A C N is the subset of selected clients in round t. This process of federated training closely resembles that of the usual FI. (FedAvg), with two key differences: the integration of the Mixup technique and adaptive sample size-weighted aggregation.
Mixup. Mixup (Zhang et al., 2018) is data augmentation technique exhibiting strong robustness to label noise. Given two samples (#t, v E and (af, v²)) in dataset Dj, it gen- crates a new sample (Øk, (k) by linearly combining the two original samples with a random weight. Specifically, the new sample o& and its corresponding label ÿ are given by:
Adaptive Sample Size-weighted Aggregation (ASSA). The proposed ASSA appears at steps ① 7 and @ in Figure 1, where the weight coefficients for each client in the global model update are determined by the number of samples actually participating in the federated training, rather than being fixed based the size of the client's entire dataset. This approach ensures that only the samples actively in- volved in the federated learning process influence the global model's weight update. ASSA demonstrates robustness to label noise, particularly for clients with high levels of label noise. The details of the weight update steps are presented in the subsequent sections.
ã = x x + (1 2) 2
セ= A y (1 X) ykk
where A is random hyperparameter drawn from the Beta distribution B(a,a) a), and is a parameter that controls the shape of the distribution. In our experiments, we set a = 1. Mixup effectively mitigates the negative impact of noisy labels by smoothing the labels and is robust to label noise. Suppose v represents noisy label and yd is the true label. According to the Mixup formulation Eq (2), the new label Uk is weighted combination of the two original labels y' and v Specifically, when À is small, ÿs will be closer to the true label v/. thus diminishing the influence of the noisy label v This label smoothing property of Mixup reduces the negative effects of label noise, making the model more robust such noise.
3.3. Preprocessing Stage
During the preprocessing stage, we utilize the CNLL to identify clean samples for each client. Only the selected clean samples are subsequently used in the federated train- ing process of this stage.
3

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Add inferred labels. We first let each client k execute CNLL locally to train local model l/- Subsequently, each additional inferred label
SUB-STAGE I: CORRECTION DOMINATED BY INFERRED
LABELS. In this sub-stage, the limited number of clean samples selected during the preprocessing stage results in insufficient accuracy of the trained global model. To ad- dress this, we introduce inferred labels as supplementary information.
sample zi. E Dr is assigned Vi based on the predictions of this local model. As a re- sult, in addition to the annotation label v each sample oi is also assigned an inferred label gh. At this stage, we represent the local dataset of each client as DE- - { v g Io (z())
Theorem 3.1. Incorporating inferred labels The as prior information refines the model's predictions, leading a performance improvement bounded as:
Select clean samples. We select the samples whose anno- tation labels and inferred labels are identical. These samples are considered clean and denoted by:
(5) where ò quantifies the reduction in prediction error after combining inferred labels and A indicates the prediction accuracy.
A(ik: whe 2 A((i)) +
D = (aak vks Th) E Dalvk = Vie
The remaining samples in Dx \ DE are deemed disputed samples. Note that, the process of selecting clean samples, even if sample with a noisy annotation label is mistakenly included in the clean set (2 due to the wrong prediction by the local model 0g (i.e., 0x's predicted label of the exam- ple is the same as its noisy annotation label), the impact of this noisy label on the global model can be mitigated by the application of Mixup.
Proof. For sample (rt vis v2). Let d be the global model 07 prediction and y (true) be the true label. Using Bayes' theorem, we update the global model's prediction as:
P(TllV)) = P D P(U)) P(T)))
(6)
This shows how incorporating the inferred label oy as prior information refines the global model's 's prediction. We refine the predictions using the inferred labels as priors, expressed through the MAP (maximum a posteriori) estimate:
ASSA. Finally, we train the global model over T1 rounds using these selected clean samples (Di) N on all clients. The weighting coefficients in ASSA at this stage are deter- mined by the size of the clean dataset Dj selected by each client. The specific weight update is as follows:
arg max P lTt)
9i.
Then, we can define the error of the global model as:
D
w²
(4)
c= S
1 Σ I(ok + v (true)),
(8)
KEN
LiEN'
Applying ASSA at this stage exhibits strong robustness, par- ticularly for clients with high levels of label noise. Specif- ically, consider a client k that experiences significant amount of label noise, resulting in relatively small clean sample set (D(). In ASSA, the weight of each client's lo- cal model in the global model update is proportional to the size of its clean sample set. Consequently, the influ- ence of clients with smaller clean sample sets on the global model is correspondingly reduced. In other words, even if the local model trained by the client on its limited clean samples contains some degree of error, its contribution to the global model's weight will be relatively minor (namely [
where I[(-) is the indicator function and S is the number of samples. After incorporating the inferred labels as priors, the error becomes:
s L
d S
(ol + v (true)),
(9)
1=1
Therefore, using the inference label as "prior information" improves the accuracy A of the model as follows:
A(Ok T..) 2 A(O + 5, 8=c-é. é.
(10)
To rigorously quantify the reduction in prediction errors, we provide detailed proof in Appendix B, linking the error reduction to the Kullback- divergence between the model's prior and posterior distributions.
EiENt [ 1), thereby mitigating the negative impact of label noise on the global model.
3.4. Label Noise Correction Stage
For controversial samples (samples whose the annotation labels conflict with their inferred labels), we propose a novel collaborative per-sample loss:
To improve the accuracy of label correction and prevent overcorrection, we propose a two-stage label noise correc- tion scheme. It consists of two sub-stages that are dominated by inferred labels and by the global model, respectively.
(ool Th. 0i:0%) = Lan (vi, H1. (1.0%))
- Lin (vk. H4. 1(.0%)
(11)
4

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Here, Lan (vh: Mi: 02) represents the annotation label loss, measuring the discrepancy between the annotation label of sample zi and the model's prediction. This loss is typically larger if there is noise in the annotation label. fim (vk, ii;; 0%) ) denotes the inferred label loss, quantify- ing the misalignment between the sample's annotation and its assigned inferred label. When the inferred label is closer to the true label or more consistent, this loss is smaller.
model's prediction is consistent with the inferred label. This is because: i) If the model's predicted label aligns with the inferred label, it indicates that the model has effectively learned the pattern of the inferred label. The inferred label likely represents the true class of the sample or is closer to it. ii) If the collaborative per-sample loss is large under these conditions, indicates a significant mismatch between the annotation and inferred labels, suggesting that the an- notation label is likely incorrect and should be corrected by the inferred label. Conversely, if the model's prediction conflicts with the inferred label, then the inferred label may not be reliable, and correcting the annotation label based on it could lead to errors. Thus, the collaborative per-sample loss should only be used to guide label correction when the model's prediction aligns with the inferred label. The specific steps for Sub-stage are as follows:
Consequently, collaborative per-sample loss serves two main purposes. On the one hand, the collaborative per- sample loss can be interpreted as measure of the con- sistency and confidence between the two labels (annota- tion label vs. inferred label). If a sample's 's annotation and inferred labels are consistent, i.e., Lan (vk= Mi 02)) = Lm (v%, 02: ( ) then - Te. 04: o2 = O, implying that the sample has minimal label noise and both labels are reliable. This provides a theoretical basis for classify- ing samples with matching annotation and inferred labels as clean samples during the preprocessing stage. On the other hand, collaborative per-sample loss also quantifies the degree of inconsistency between the annotation label and the inferred label. When the annotation label is erro- neous (i.e., contains noise). aan (vk. Rh: ( will be larger while (V 93; 0%) remains relatively smaller, leading to larger Loo Tye 0h: 08 ). In conclusion, The greater the collaborative per-sample loss (Ceo (yk. Tke f 0'2' > 0), the larger the discrepancy between the annotation and in- ferred labels, indicating that the annotation label is likely incorrect and can be corrected by the inferred label. We provide further explanation in Appendix A for choosing per-sample loss over using labeled per-sample loss alone.
Calculate the collaborative per-sample loss. To opti- mize computational efficiency, we calculate collaborative per-sample losses only for those samples where the global model's prediction matches the inferred label. For cach client, these samples are recorded as the set:
DE = [(rk v Tl. E D& Dila = ).
(13)
Then, each client locally computes Gaussian Mixture Model (GMM) on the collaborative per-sample loss val- ues for all samples in the set Dg to partition the set into two subsets: subset D that can be corrected by inferred labels and a disputed subset D
Filter samples for correction. To avoid overcorrection, we apply the correction only to those samples identified as having a high-confidence inferred label. We introduce correction rate 01 where for client k. we select the top Ti ppeceent of samples from D that have the highest col- laborative per-sample loss. These samples are then relabeled using the inferred label yh. The subset of samples to be relabeled is denoted by:
Theorem 3.2. In collaborative per-sample losses, inferred labels provide additional stability, allowing for more accu- rate estimates of true labels.
proof. Bayes' theorem allows us to compute the posterior probability of the annotation label v given the model's pre- diction ij and the inferred label vy. Therefore, the posterior probability of Eq. (1I) can be written as:
D arg
max DED T
LCo (D;
(14)
P(vk Di. T x
(12)
For correct annotations, both P(O (uf)) and P b will be high. For noisy annotations, P(oivv)) will be low, but P(vilud will still provide a stable signal, allowing us to better estimate the true label. This shows that the inferred label P% provides additional stability when the annotation label is noisy, allowing for more accurate estimation of the true label. (For more detailed stability analysis and proof, see Appendix C.)
ASSA. Finally, the global model is improved using the corrected samples by updating the parameter over T2 rounds. In this stage of federated training, the global model 0%²² updates its weights w using ASSA as follows:
DE + D
w²
w
(15)
EieN. e (1D)) + (D
REN
Validity of model predictions consistent with inferred labels. is important to note that correcting annotation labels based on collaborative per-sample loss is only meaningful if the
The weight EKEN
Exe [D D represents the cumu-
D + 1
lative impact of client i on the global model, thereby further
5

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Algorithm 1 FedClean
each sample using the global model, identify those with higher loss values, and replace their annotation labels with the model's predicted labels, thereby correcting the labels. The specific steps for Sub-stage II are as follows:
I: Input: N (number of clients), T1, T2. Ta (numher of rounds of communication), D = {Dx Ji= (dataset), &g (finitialized global model).
2: Output: Global model 0c.
//Prepracessing stage.
3: for k = 1 to N do
Calculate the per-sample loss. We first compute the per-sample loss for each sample in the remaining dataset DE = [ (ork wi T E Dr \ (Dg U Di. }} where the loss for each sample, denoted by Can (Di o O rep- resents the discrepancy between the model 0 + predic- tion and the annotation label. The aim is to correct the noisy annotation labels, and thus the loss is calculated relative to the annotation label. Then, each client locally computes a GMM on the per-sample loss values for all samples in the set DE to partition the set into two subsets: a noisy subset D and clean subset DE
4: Train model 02 on DE by CNLL;;
5: Add the inferred label vi. = (zt) to sample si,
V(rk,, v Dx;
6: Select clean samples via Eq. (3);
7: end for
8: for t= to 7i do
9: Train global model 0% on (Di) via Eq. (4):
10: end for
alab correction stage.
ISSub-ttage I: Correction stage dominated by inferred labels.
11: for k = to N do
12:
Put (=i; Wir (1)) into the set DR via Eq. (13);
13:
Calculate Lealthe Th. ūL: ( via Eq.
(11),
Table List of datasets used in our experiments.
V(ri, yh. vi) E DR:
Dataset
CIFAR-10
CIFAR-100
ClothingIM
14:
Divide DE into D and D
based on
Size
50000
50000
1000000
Lio Ve. Dci ( via GMM;
# Classes
10
100
14
# Clients
50
50
300
15: vi + Th. V(rj, Vee Tb) E DE via Eq. (14);
Rounds TT/TTTT
100/150/150
100/150/100
50/100/100
16: end for
I Learning rate
0.01
0.01
0.03
Batch size
20
17: for L = Ti + 1 to Ti + T2 do
N 18: Train global model 0g on (Di 1==1 via Eq. (15);
Architecture
ResNet-18 18
ResNet-34 34
ResNet-50
19: end for
Select samples for correction. For all samples in noisy subset Dr we identify and select only those samples with high loss values, which are likely to contain label noise. To prevent overcorrection, we introduce two parameters the correction rate 02 and the confidence threshold e. (At this correction sub-stage, the only reference available is the global model, which is not fully reliable. To enhance its reli- ability, a confidence threshold is applied. In the sub-stage I, noise labels are corrected using both the inferred label and the inferred model, which has proven reliable enough in the- orem 3.1 to not require an additional confidence threshold.) Specifically, for client k we first identify the top o2 pe-percent of samples from D that have the highest collaborative per-sample loss, denoted by:
I/Sub-stage HI: Correction stage dominated by global model.
20: for k =1=1 to N do
21: Calculate Lan(ye Uk: )T2), V(rk. vi)
E
DE
{( (xx V Fe)
E Dr \ (D) D }
22: Divide DE into DE and D
based
on
Cmi 02: (2:))=+20000 via GMM;
23: Put (xk, v into the set DE via Eq. (16);
24: v/. > 0%. V(ri, vi) E D via Eq. (17);
25: end for
26: for (or Ti + T2 to Ti T2 do
27: Train global model 0g on (D h via Eq. (18);
28:
end for
29: Return Oc 0% TT
mitigating the negative effects of noise correction errors at Ti
D = arg
max DCD D |P
Lan (D,
(16)
this stage. For client k, if D V. then w +- ukk"
SUB-STAGE II: CORRECTION DOMINATED BY GLOBAL LABELS. In the first sub-stage, we perform preliminary la- bel corrections using a strategy led by the inferred label and assisted by the global model, which improves the accuracy of the global model. However, since the global model may not be fully optimized during the initial training phase, there may still be small number of samples that are not effec- tively corrected. To address this, in the second sub-stage, we employ global model-led label correction method to further enhance label accuracy. The primary objective of this sub-stage is to refine the sample labels using the glob- ally trained model. Specifically, we calculate the loss for
Next, we compute the prediction vector (G,(D)) (DR) from the global model and select the sample for re-labeling only the maximum value in 6G, (DR) exceeds the confidence threshold e. Thus, the subset of samples to be re-labeled is denoted by:
D = [a vi) ED max ( +To (xx))) 2e}.
(17)
Finally, we use the global model 87 Ta's predicted labels (i(s) to correct the annotation labels (yf s) of the samples in the subset DD
6

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Table 2. Average (5 trials) accuracies (%) of various methods on CIFAR-10 10 dataset with IID and non-IID settings at different noise levels (o: ratio of noisy clients, T: lower bound of client noise level). The best results are highlighted in bold.
IID
non-IID
Methods
p=0 0
p= 1 = 0.5
p=0
P=0.5
T= 0
0.3
T 0.5
==0
7=
FedAvg
83.16 0.19
38,36
2.21
90.04
2.17
82.61
0.266
34.65 4 1.53
FedProx
9
82.45 a: 0.27
35.21 4: 1.75
90.82
2 0.18
81.76
0.22
32.84
1: 1.65
FedCor
91.12 0.28
0.30
47.49 + 1988
90.21
+ 0.16
89.11
0.22
39,40
1.51
FodNoRo
0.19
38.48.00.22
32.18 898
88.91 0.20
86.99 0.21
30.21 # 1.72
FedBeat
0.22
36.13 8.29
89.55
0.29
33.20
1.62
FedELC
8.26
0.26
35.72 2.10
09.1
31.95 03.25
1.80
FedFixer
87.06 0.47
62.87 + 0.17
0.32
87.82
59.01 = 0.55
FedClean
0.17
0
81.68
2.10
81.79 0.200
36.53.10.25
77.12 1.95
FedClean"
0.19
88.41
0.227
83.75 a 2.03
89.34 a 0.21
86.79 0.223
80.55 1.82
ASSA. After correcting the annotation labels in the second stage, we train the global model over T3 rounds using the these corrected labels and the clean samples in D We continue to leverage the cumulative impact of clients on the global model in ASSA as follows:
Table 3. Average (5 trials) accuracies (5) of various methods on CIFAR-I00 dataset with IID setting at different noise levels (p: ratio of noisy clients, T: lower bound of client noise level). The best results are highlighted in bold.
Methods
p=
p= 0.5
p=
==
7= 0.3
== 0.5
FodAvg
72.36=
31.44 62.12:0.25
+
+ D ++
FedProx
72.04
63.53 0.20
32:51 1 0.88
w'< Σ
wt. (18)
FedCorr
72.33
72.40
40.92200.75
REN'
EiENi (1;)) DD
DE
(Dx)))
FedNoRo
FedBeat
0:22
7.282
68.01
38.12 6..02
30.74 0.26
FedELC
71.82=0.18
70.16
45
FodClcan
6..86=0.33
68.75 0.22
63.11 0.92
For client kt if Df UD = 0. then w + w To.
FodClean
0.294
0.11
66.54 + 0.84
3.5. Algorithm
The evaluation encompassed specific experimental param- eters, including the number of rounds. model architecture, total number of clients, proportion of selected clients per dataset, batch size, and learning rate, as detailed in Table I. For optimization, we employed an SGD optimizer with a momentum of 0.5 and a cross-entrops loss function on the client side. The data partitioning and noise model followed the approach in FedCorr (Xu et al., 2022), where p E (0, 1) denotes the class sampling probability of clients, p E [0.1] denotes the proportion of noisy clients (with
The pseudo-code of the FedClean method is shown in Algo- rithm 1, which is easily mapped into the technical content in preprocessing stage (lines 3-10) and label correction stage (Sub-stage I: lines 11-19. Sub-stage II: lines 20-29).
4. Experiments
We first conduct experiments on benchmark datasets with varying label noise settings to make comprehensive com- parisons with state-of-the-art methods. Then, we design ablation studies to show the features of FedClean.
indicat- ing all clients are noisy), and 7 E [o. 1] represents the lower bound of the noise level for noisy clients.
4.2. Overall Comparison Results
4.1. Experimental Setup
We compared the predictive performance of FedClean with the aforementioned methods under label noise conditions, evaluating both IID and non-IID distributions on CIFAR- 10, as well as IID distribution on CIFAR-100. The results summarized in Tables 2 and 3, respectively. In both IID and non-IID settings, when all clients are noise-free, all methods exhibit similar strong performance. However, when noise is introduced, the performance of these methods diverges significantly. FedCorr performs well when not all clients are noisy, but when all clients are noisy, only our methods (FedClean' and FedClean?) maintain good performance. Other methods show marked decline in performance under this condition. This supports our earlier claim that FedClean does not rely on the presence of clean clients, demonstrating robust performance even with a high proportion of noisy clients, as further discussed in Subsection 4.3. Additionally, although the performance of FedClean is not optimal when
Methods in comparison. We compared the proposed FedClean method with two baseline approaches. FedAvg (McMahan et al., 2016), FedProx (Li et al., 2020b), well five state-of-the-art methods: FedCorr (Xu et al., 2022), FedNoRo (Wu et al., 2023), FedBeat (Wang et al., 2023), FedFixer (Ji et al., 2024), FedELC (Jiang et al., 2024). For the CNLL employed in FedClean, we selected one representative method from each of the two key ap- proaches discussed in Subsection 2.1: Co-teaching (Han et al., 2018) (FedClean') and Joint Optim (Tanaka et al., 2018) (FedClean?).
Implementation details. We evaluated different approaches under both IID (Independent and Identically Distributed) (Krizhevsky, 2009)) and non-IID (non- Independent and Identically Distributed (Ma et al., 2022)) (CIFAR-10 10, I (Xiao et al., 2015)) data settings.
7

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Table 4. Accuracies (%) of various methods on Clothing M with non-IlI setting.
Methods
FedAvg
FedProx
FedCorr
FedNoRo
FedlBeat
FedELC
FedFixer
FedClean
FedClean
Acc
68.63
69.15
69.02
69:21
67.05
69.24
70.52
70.17
72.39
not all clients are noisy, the difference between FedClean and the optimal performance is minimal. We attribute this slight performance gap to the use of relatively basic CNNL methods in our implementation. With more sophisticated believe FedClean has significant potential for further improvement. We also conducted ex- periments in a non-IID setting using the real-world dataset Clothing M. Notably, we did not introduce synthetic label noise, as the dataset already contains inherent tag noise, with all clients being noisy clients (i.e., p= = 1). The experi- mental results, presented in Table 4, show that the proposed method FedClean" 2 achieves the highest accuracy.
4.4. Ablation Study
Table 5 outlines the impact of the components in FedClean. We summarize key insights into FedClean's 's effectiveness: i) All components contribute to improved accuracy. ii) CNLL has the greatest influence. During the preprocessing phase, CNLL essential for accurately selecting clean samples; only when clean samples are correctly identified can subse- quent corrections be effective. iii) The robustness of Fed- Clean to FL with a high proportion of noisy clients is pri- marily achieved through the local CNLL applied at clients.
CNNL. approaches,
5. Extension of ASSA
4.3. Robustness When All clients Are Noisy
ASSA can be seamlessly extended to strengthen privacy pro- tection. Inspired by ideas from zkCor (Wang al., 2024), we employ the knowledge proofs (ZKP) (Sun et al., 2021) for secure label noise correction in our FedClean. By requiring each client to provide a computation integrity proof, zkCor ensures the correctness of the label correction process while preserving privacy. This integration adds a layer of privacy protection, preventing malicious clients from affecting the global model with erroneous or tampered labels. Additionally, zkCor introduces a batch ZKP protocol that enhances verification efficiency. By allowing proofs to be verified in batches, we reduce the computational load on the aggregator, improving scalability. This batch verifica- tion is incorporated into ASSA, alleviating the aggregator's s verification burden. zkCor is detailed in the Appendix D. (Notably, zkCor is an extension of ASSA designed to meet enhanced privacy requirements.)
HID (c-0.5)
nen-IID (y-0.3)
60
FESCOT
Pechoat
Figure 2. Accuracy (%) variations of various methods the CIFAR-I0 dataset as the ratio of noisy clients (p) increases under IID (with r 0.5) and non-IID (with 0.3) settings.
Figure 2 illustrates the performance variations of various methods the CIFAR-10 dataset as the ratio of noisy clients p increases, with fixed lower bounds for the noise level under both IID and non-lID settings. The results show that when P < 1, all FNLL methods perform significantly better than the baseline, with FedCor consistently achiev- ing the best performance. However, when p = 1 (i.e., when all clients are noisy), only the FedClean methods main- tains strong performance, while the performance of all other methods deteriorates sharply. Furthermore, the trend of per- formance decline reveals that the FedClean method exhibits the slowest decline, particularly when p increases from 0.8 to 1. This highlights the robustness of FedClean, demon- strating its resilience against high ratio of noisy clients in FL, even in the absence of clean clients.
6. Conclusion
In this paper, we introduced FedClean, a robust label noise correction method for FL that does not rely on the presence of clean clients. FedClean employs two-stage framework to identify and correct noisy labels, leveraging collabo- rative per-sample loss function to assess the confidence of label corrections. Additionally, we proposed ASSA to opti- mize the influence of each client during federated training, based on their contribution to the dataset size. Our exper- imental validation on the CIFAR- 10/100 and Clothing 1M datasets under both III and non-III conditions demon- strates the effectiveness of FedClean in mitigating label noise without compromising privacy.
Table 5. Ablation study results (average and standard deviation of trials) on CIFAR- of FedClean?
IID
non-IID
Methods
p =
0.5
P
p=
p =
0.5
p 1
0.3
0.5
0.3
0.5
Ours
88.41
83.75
86.79
80.55
While promising, our approach leaves room for exploration, particularly in refining the local CNLL model and address- ing its complexity. Future work will optimize the CNLL process for clients and explore advanced techniques to en- hance label noise correction in federated settings.
Ours w/o CNLL
77.52
47.74
71.30
40.11
Ours w/o correction
81.02
76.45
71.71
73.94
Ours w/o correction
86.83
82.58
84_37
79.19
Ours wio correction II
84.61
79.85
82.31
77.01
Ours w/o ASSA
87.77
82.91
85.98
79.28
Ours w/o Mixup
88.12
83.55
86.18
80.00
8

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Acknowledgments
for oecumenical noninteractive arguments of knowl- edge. IACR Cryptol. ePrint Arch., 2019:953, 2019. URL https: http::://aa..eeeeee orq/ hets/CoppusD:::: 2
This work was sponsored by the SEU Innovation Capabil- ity Enhancement Plan for Doctoral Students [CXJH SEU 24243], the National Natural Science Foundation of China [Grant 62076130J, the Start-up Research Fund of South- east University [Grant RF RF1028623059), and the Big Data Computing Center of Southeast University.
sta.ppp.smaticccccoo....e.. 201 6855388.
Guo, Y., Xie, H., Miao, Y., Wang, C., and Jia, X. Federowd: A federated and privacy-preserving crowdsourcing plat- form blockchain. IEEE Transactions Services Com- puting, 15(4):2060-25) 2073, 2020.
Impact Statement
Han, B., Yao, Q. Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I. W.-H., and Sugiyama, M. Co-teaching: Ro- bust training of deep neural networks with extremely noisy labels. In Neural Information Processing Systems, 2018. URL https: /ppi. semant iiciiicsss isscholar. org/ htsp/CorpuuDD.. 6204546..
This paper presents work whose goal advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
References
Huang, W., Ye, M., Shi, Z., Li, H., and Du, B. Rethinking federated learning with domain shift: A prototype view. In 2023 IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pp. 16312-16322. IEEE, 2023.
Awosika, T., Shukla, R. M., and Pranggono, B. Trans- parency and privacy: the role of explainable ai and feder- ated learning in financial fraud detection. IEEE Access, 2024.
Ji, X., Zhu, Z., Xi, W., Gadyatskaya, O., Song, Z., Cai, Y., and Liu, Y. Fedfixer: Mitigating heterogeneous la- bel noise in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 12830-12838, 2024.
Bai, L., Hu, H., Ye, Li, Wang, L., and Xu, J. Member- ship inference attacks and defenses in federated learning: A survey. ACM Computing Surveys, 57(4):1-35, 2024.
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C. A. Mixmatch: A holistic approach to semi- semi-supervised learning. In Wallach, H., Larochelle, H., Beygelzimer, A., d' A'Ach--Buc, F., Fox, E., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL
Jiang, X., Sun, S., Wang, Y., and Liu, M. Towards federated learning against noisy labels via local self-regularization. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 662-873, 2022.
oo tee/pape__fiieee /ttppppeeffleeeepppp//////////// ngs. neurips. pdf.
Jiang, X., Sun, S., Li, J., Xue, J., Li, R., Wu, Z., Xu, G., Wang, Y., and Liu, M. Tackling noisy clients in feder- ated learning with end-to-end label correction. In Pro- ceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM '24, pp. 1015-1026, New York. NY, USA. 2024. Associa- tion for Computing Machinery. ISBN 9798400704369, doi: 10. 10.1145/362767333675556 URL ht /doi 1ttp:/..114/3322776 36795555
Chen, Y., Yang, X., Qin, X., Yu, H., Chen, B., and Shen, Z. Focus: Dealing with label quality disparity in fed- erated learning. In Federated Learning, pp. 108-121, 2020. URL https: org/Corpus 22I.D::21199999
Fang. X. and Ye, M. Robust federated learning with noisy and heterogeneous clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10072-10081, 2022.
Kate, A., Zaverucha, G. M., and Goldberg, I. Constant- size commitments to polynomials and their applica- tions. In International Conference on the Theory and Application of Cryptology and Information Security, 2010. URL https: //ppi.appp.//////// semanticscholas org/ CCorpuSTD:22319770
Fu, S., Xie, C., Li, B., and Chen, Q Attack-resistant feder- ated lcarning with residual-based reweighting. In AAAI Workshop Towards Robust, Secure and Efficient Machine Learning, 2021.
Kim, S., Shin, W., Jang, S., Song, H., and Yun, S.-Y. Fedm: Exploiting k-reliable neighbors towards robust federated learning. In Proceedings of the 31st ACM International Conference on Information Knowledge Management, pp. 972-981, 2022.
Gabizon, A., Williamson, Z. J., and Ciobotaru, O.- M. Plonk: Permutations over lagrange-bases

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Krizhevsky, A. Learning multiple layers of features from tiny images. 2009. URL https://api. semanticscholats org/ httgg//orpssD:::: : 8268744
Sharma, A. and Marchang, N. A review on client- server attacks and defenses in federated learning. Comput. Secur, 140:103801, 2024. URL https: //api. stapiissmmniccccool.... wtwpaaa..ggg//ooooaaeeeeeee 269987688.
Li, J., Socher, R., and Hoi, S. C. H. Dividemix: Learn- ing with noisy labels as semi-supervised learning. In International Conference on Learning Representations, 2020a.
Song, H., Kim, M., and Lee, J.-G. Selfie: Re- furbishing unclean samples for robust deep learning. International Conference on Machine Learning, wwm.nticscho......
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Tal- walkar, A., and Smith, V. Federated optimization in heterogeneous networks. In Dhillon, I., Papail- iopoulos, D., and Sze, V. (eds.), Proceedings of Machine Learning and Systems, volume 2, pp. 429-450, 2020b. URL
2019. URL httpss//ap...mmttiicc... org 0tts/CoopuII::170000000
Song, H., Kim, M., Park, D., Shin, Y., and Lee, J.-G. Learn- ing from noisy labels with deep neural networks: A sur- vey. IEEE transactions on neural networks and learning systems, 34(1 11:1113888888811 8153, 2022.
wtw.:/eeeediii..ml.l....... mlsys. httpppaperrilllpapppr/////////// 1 1F5fe83398803336ee644777772 -5 pdf.
Sun, X., Yu, F. R., Zhang, P., Sun, Z., Xie, W. and Peng, X. A survey on zero-knowledge proof in blockchain. IEEE network, 35(4):198- 190-20,, 2021.
Li, Y. and Wen, G. Research and practice of financial credit risk management based on federated learning. Engineer- ing Letters, 31(1), 2023.
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K. Joint optimization framework for learning with noisy labels. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5552-5560, 2018.
Liu, J., Huang. Zhou, Y., Ji, S., Xiong, H., and Dou, D. From distributed machine learning to federated learning: A survey. Knowledge and Information Systems, 64(4):885-91 2022.
Thummisetti, B. S. P. and Atluri, H. Advancing healthcare informatics for empowering privacy and security through federated learning paradigms. International Journal of Sustainable Development in Computing Science, 6(1): 1-16, 2024.
Ma, X., Zhu, J., Lin, Z., Chen, S., and Qin, Y. A state- of-the-art survey on solving non-iid data in federated learning. Future Generation Computer Systems, 135: 244-258, 2022.
Wan, C. P. and Chen, Q. Robust federated learning with attack-adaptive aggregation. In International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI (FTLIJ- CA2 2021.
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. Communication-efficeeee learning of deep networks from decentralized data. In Interna- tional Conference Artificial Intelligence and Statistics, 2016. URL https:///// http:///a.ppppaa...eeee..e.. org/CorpusID:IIe 149555344
semanticccoolee..
Wang. H., Jiang, T., Guo, Y., Guo, F., Bie, R., and Jia, X. Label noise correction for federated learning: A secure, efficient and reliable realization. In 2024 IEEE 40th International Conference on Data Engineering (ICDE), pp. 3600-3612. IEEE, 2024.
Nevrataki, T., Iliadou, A., Ntolkeras, G., Sfakianakis, I., Lazaridis, L., Maraslidis, G., Asimopoulos, N., and Frag- ulis, G. F. A survey on federated learning applications in healthcare, finance, and data privacy/data security. In A/P Conference Proceedings, volume 2909. AIP Publishing, 2023.
Wang, I.., Bian, J., and Xu, J. Federated learning with instance-dependent noisy label. ICASSP 2024 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8916-8920, 2023. URL https: //api. semanticscholar org/ Corppo
Rani, S., Kataria, A., Kumar, S., and Tiwari, P. Federated learning for secure appl-applications in smart healthcare systems: A comprehensive review. Knowledge-buser systems, 274: 10658, 2023.
Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., and Bailey, J. Symmetric cross entropy for robust learning with noisy labels. In 2019 IEFE/CVF International Conference on
Rjoub, G., Wahab, O. A., Bentahar, J., and Bataineh, A. Trust-driven reinforcement selection strategy for feder- ated learning on iot devices. Computing, 106(4):1273- 1295, 2024.
Computer Vision (ICCV), pp- 322-330, 2019. doi: 10. 1109//CCV.209.0000 201400011.
10

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Wang, Z., Zhou, T., Long, G., Han, B., and Jiang, J. Fednoil: A simple two-level sampling method for federated learn- ing with noisy labels. arXiv preprint 2022.
Zeng, B., Yang, X., Chen, Y., Yu, H., Hu, C., and Zhang, Y. Federated data quality assessment approach: robust learning with mixed label noise. IEEE Transactions Neural Networks and Learning Systems, 2023.
101100
Wen, J., Zhang, 7., Lan, Y., Cui, 7.,, Cai, J., and Zhang, W. A survey on federated learning: challenges and applica- tions. International Journal of Machine Learning and Cybernetics, 14(2):51-5533 2023.
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz. D. mixup: Beyond empirical risk minimization. In International Conference Learning Representations, 2018. URL https: /oppnreview net /ooum? id=r1Ddp1-Rb
Wu, N., Yu, L., Jiang, X., Cheng, K.-T., and Yan, Z. Fed- noro: Towards noise-robust federated learning by address- ing class imbalance and label noise heterogeneity. In- ternational Joint Conference Artificial Intelligence, 2023. URL https: wtt.aap..aaaaaa.........e.aaeaae www.nticscoolll......ooo org/ /oto:oopppD:::::5450333
Xiao, T. Xia, T., Yang. Y., Huang, C., and Wang, X. Learn- ing from massive noisy labeled data for image classifica- tion. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2691-2699, 2015. doi: 10.1109/VVPR.2005.77990
Xu, J., Chen, Z., Quek, T. Q., and Chong, K. E. Fedcorr: Multi-stage federated learning for label noise correction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10184-10193, 2022.
Xu, X. and Lyu, L. reputation mechanism is all you need: Collaborative fairness and adversarial robustness in fed- erated learning, In International Workshop Federated Learning for User Privacy and Data Confidentiality in Conjunction with (CML(FL-ICML 2020.
Yadav, S. P., Bhati, B. S., Mahato, D. P., and Kumar, S. Federated learning for 1OT applications. Springer, 2022.
Yang, M., Qian, H., Wang, X., Zhou, Y., and Zhu, H. Client selection for federated learning with label noise. IEEE Transactions on Vehicular Technology, 7102)2293-2197, 2021.
Yang, S., Park, H., Byun, J., and Kim, Robust federated learning with noisy labels. IEEE Intelligent Systems, 37 (2):35 2022.
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I. W.-H., and Sugiyama, M. How does disagreement help generaliza- tion against label corruption? In International Confer- ence on Machine Learning. 2019. URL https: / semant escholas ar. org CorpusTD: 5931 6631.
Zacem, R. N. and Barber, K. S. The effect of the gdpr on privacy policies: Recent progress and future promise. ACM Transactions on Management Information Systems (TMIS), 12(1):1-20, 2020.

---

APPENDIX
Supplemental Information for FedClean
A. Why Choose Collaborative Per-sample Loss ?
We explain our choice measure the confidence of noisy labels using cooperative per-sample loss, rather than relying solely on annotation label per-sample loss, from the following perspectives:
Noise in Annotation Labels: If there is noise in the annotation label, Can (vk. M1: 0 will typically be large. The loss Li v 0%) ), which reflects the consistency of the inferred label with the category of the sample, generally smaller when the inferred labels more consistent and accurate, as inferred labels are formed by grouping similar samples together.
Increase in Collaborative Loss: (yk 6 exceeds (ik o6: ( the collaborative per-sample loss L (yk. Te. 0f; 0²) will be larger, indicating that the annotation label is likely noisy. This increases the likelihood that the inferred label can provide a more accurate correction.
Correction Potential: When the collaborative per-sample loss is large, the difference between the annotation label and inferred label significant, suggesting that the sample can be corrected by the inferred label. Therefore, larger collaborative per-sample loss implies stronger correction effect of the inferred label on the sample.
B. Extended Proof of Theorem 3.1
Theorem 3.1. Incorporating inferred labels oy as prior information refines the model's predictions, leading to a performance improvement bounded as:
Aliie vl)) A(D())
(19)
where 5 quantifies the reduction in prediction error after combining inferred labels.
Proof. Let (arks via vit) be the i-th sample in the k-th client, where - is the input feature, y (true) is the true label, and zh is the inferred label (or prior information). Let the global model of = = (zt) represent the prediction made by the unrefined model.
aim to refine the prediction by incorporating ,i as prior. The process updating the model's prediction based on this prior grounded in Bayesian inference. Specifically, we update the posterior distribution of the model's 's prediction given the inferred label using Bayes' Theorem:
P(T ( P(D))
P(
(20)
P(
where:
P( ) is the posterior probability of the prediction given the inferred label;
P ( is the likelihood of observing b given the prediction of:
P P is the prior probability of the prediction o from the global model;
P is the normalizing constant ensuring that the posterior sums 1.
12

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
The update process refines the global model's prediction using the inferred labels as priors. The refined prediction is obtained by maximizing the posterior:
y (Th)
arg max P(y lvt)
(21)
Vi
which is equivalent MAP (performing Maximum A Posteriori) estimation.
To analyze the model's error, define the error rate of the global model before incorporating the inferred labels as:
8 Σ
c
1(0 + y (true)),
(22)
where I1()) is the indicator function and is the total number of samples.
After incorporating the inferred labels priors, the error rate becomes:
5 Σ (Vl + yk (true)),
(23)
where if (ot) is the refined prediction after updating with the inferred label vi-
Now, let us express the accuracy of the model before and after incorporating the inferred labels. The accuracy without the prior is:
A A = -,
(24)
and the accuracy after incorporating the inferred labels is:
A 0k.) = -1-6. E..
(25)
Thus, the improvement in accuracy is:
8 6
[[okk vt (true)) ((t) A (true)))
(26)
5
We guaranteed that 2 since incorporating prior information cannot worsen the model's prediction.
In order to quantify the reduction in prediction error more rigorously, we can relate the error reduction the KL (Kullback-
Leibler) divergence between the model's 's prior and posterior distributions.
The KL divergence between the prior and posterior distributions of the prediction of given the inferred label of. is defined as:
P(ú ( P(O))
(P(
log
(27)
This measures the informational difference between the prior and posterior distributions. A smaller KI. divergence indicates that incorporating the inferred labels has effectively refined the model's predictions.
Thus, we can use the following bound on the improvement in accuracy:
S Σ D
8 8=
(28)
where the approximation assumes that the reduction in error is closely related to the KL divergence between the prior and posterior distributions.
This formulation provides a more nuanced view of the improvement in prediction accuracy by leveraging the Bayesian framework. Specifically, the reduction error is not just a simple difference prediction success, but is linked to the information gain obtained from the inferred labels.
13

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Therefore, incorporating inferred labels is prior information refines the global model's predictions and leads to an improvement in accuracy bounded
A(O v
6G, 8 ô=c-é and 8 DeL (P(N
(29)
The accuracy improvement is hence driven by the Bayesian updating process, and the reduction in prediction error is quantitatively related to the information gain through the KL divergence between the prior and posterior distributions.
C. Extended Proof of Theorem 3.2
Theorem 3.2. In collaborative per-sample losses. inferred labels provide additional stability, allowing for more accurate estimates of true labels.
Proof. We aim to compute the posterior distribution P(yíli li Wh). which represents the belief about the annotation label v given the model's prediction i and the inferred label V.
By Bayes' theorem, we can express the posterior distribution as:
P(y W mh) POAL
(30)
where:
P( v is the likelihood of observing the model's prediction given the annotation label;
P v is the likelihood of observing the inferred label given the annotation label;
P(y)) is the prior.
Now, let analyze how the inferred labels provide stability. We introduce the Bayesian evidence in the form of the marginal likelihood of the inferred label of. by marginalizing out v/::
P = Σ
(31)
Vi
which can be interpreted as the expected likelihood of Th accounting for possible true labels weighted by their posterior probability given -j- The inferred label Vo plays crucial role in stabilizing the model's estimates, particularly in scenarios where annotations noisy. When the likelihood P(t lof) is low due to noise, the presence of reliable inferred label y can compensate for this uncertainty and improve the overall estimate quality.
To quantify this stability, we introduce the expected log-likelihood as a measure of model uncertainty:
Sstability = E P(v (g) [log P(y 10 v.).
(32)
This quantity reflects the model's confidence in its estimates of vf- and a higher value indicates greater stability. When P(Alee low (due to noisy annotations), the inferred label 8) increases the likelihood the annotation label y thus increasing the stability of the model's 's predictions.
We can also formalize the stability improvement using the KI. divergence, which measures the difference between the posterior distribution and the prior distribution:
P(v 11 vi.) P(y))
DxL. Dra(((ri (P (((()) Eroggog .tt) log
(33)
The reduction in KL divergence after incorporating Ve reflects the improvement the model's stability and accuracy. smaller KL divergence implies that the model's posterior has become closer to the true distribution of y due to the stabilizing effect of -
14

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Protocol (z be security parameter.
Step 1: Given the security parameter 2, the public parameters pp are generated.
Step 2: For each client the private dataset is committed using the Dataset Commitment protocol described in Subsection D.1.
Step 3:
Each client performs FedClean on datasct D, to generate the arithmetic circuit and corresponding wire values as witnesses M,, (2) Each client performs Proof Generation (Subsection D.3) with the private dataset D₂- witnesses Hj arithmetic circuit C, and public parameter pp, producing the proof and value needed in ASSA.
Step 4: The aggregator performs Batch Verification (Subsection D.3) using proofs x, commitments em,, arithmetic circuit C. value and public parameter pp. If the verification succeeds, it outputs I and accepts the value; otherwise, it rejects the results and aborts.
Figure 3. The zkCor* protocol for FedClean.
Thus, we can conclude that incorporating inferred labels into the model's decision-making process leads to an improvement in stability, especially when annotations noisy. This results in accurate estimate of the annotation label v the inferred label of provides additional information that counteracts the effects of noise.
Plyilue vi) Ply 1o)). where
DxL ( (Plv 1it P(Ol) 2 0.
(34)
D. zk Cor in ASSA
We incorporate zkCor into our ASSA, and since we modified the arithmetic circuit in zkCor to align with our ASSA algorithm, the resulting protocol, referred to as zkCor", is illustrated Figure 3.
D.1. Dataset Commitment
For each client & E A, it commits to the private dataset D; using the randomness тı and the public parameter pp, generating (k) the commitment cmq (Kate et al., 2010). Specifically, for the item day in the æ-th row and y-th column,
fo 0 where == 1) y.
(35)
where n the number columns the dataset, the client applies the Kate polynomial commitment scheme to generate cmg..
D.2. Noise Correction
Our proposed FedClean noise correction scheme requires clients to upload the number of local samples involved model updates during each training iteration. The aggregator then updates the global model using the ASSA aggregation scheme, while clients must prove the computational integrity of their local label noise correction. To accomplish this, both the client and aggregator generate three arithmetic circuits C(1)- C(2)- C(3) for calculating the number of locally selected samples in Eqs (4), (15), and (18) as public information. FedClean can calculate the dataset sizes for the three ASSAs as follows:
D
I'yi = Th
(36)
D
I(vi Fi) - DDE.
(37)
2=1
15

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
DD
D nk
T(yk + m |D
IPE
(38)
In Eq 38, D2 represents the prediction label for the global model 0 +T
To verify the integrity of each client's local computation in each iteration, suffices to validate the correctness of the values
[D% (Dj and D + |D (which are updated after each local processing.) by converting Eqs (36), (37), and (38) as
follows:
n
| O,
DD
Iluk
vlt)
(39)
12
= v1t -IDAl } }
livi
(40)
1=1
D
D
+
llvi * 0% DD - 1
-0.
(41)
2=1
The above arithmetic can be expressed in the ZKP arithmetic circuit. To facilitate the operation of the counter I(-), we define the ra-diimeniional vectors ( and
(
if vi Vi if y + Th.
k
11)
0 0 i ni-
(42)
if vt 1k
(2)
the client could generate the 0si< nk-
0
if vi + Di.
Using the arithmetic circuits " c.c c computed from (Pi). (D and D |+ [D
proofs (1) k as described in the following subsection.
D.3. Proof Generation
In the proposed FedClean scenario, proofs from each client use the same arithmetic circuits C(I)- C(2). C(a). derived from subsection D.2. To prevent the aggregator from repeatedly verifying each proof, a new batch ZKP protocol is introduced in zkCor, allowing the aggregator to verify all proofs in a single process.
High-level Idea of Batch ZKP. Let { {p()))
fK=1 denote the set of clients (provers) participating the round parameter update of FedClean, where g Fraction N (with Fraction representing the proportion of clients involved in FL training). In the FedClean algorithm, the C(1) arithmetic circuit is used for 1 < t S Ti, the C(2) circuit for Ti + S t < Ti + T2, and the C(3) circuit for Ti + Tr + 1 S < Ti + T3 + Tz. The batch ZKP protocol requires distinct Lagrange basis set for (p()) k=1
when interpolating polynomials. Assuming subgroup H of order q", the highest polynomial order in the ZKP
protocol is q" - 1. p( E {pp k-1
restricted to ues (t)
(1)
(1) 111 where L; is the
(1( ([x-1)xx--142
i-th Lagrange base.
Suppose {p(1)) J==1 have witnesses vectors {
(0) k=
Their corresponding interpolation polynomials are (x) S
By applying Kate polynomial commitment (Kate et al., 2010), there are commitments { (x)]] (aggregator, the server in FedClean.) able to compute
The verifier
[x(]())]
Σ [1
(43)
(0) Let the vector that corresponding the polynomial (X) be a, which the concatenation of a random challenge E IF (F is the finite field.) chosen by the verifier, {P( J k=1
For any provide their openings at the random point
'k==
16

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
as (m)) k=1
(r) = Σ (r):
k=1
(44)
1(0 = Σ
hx (X).
1=1
where { (x) k=1
and (X) are defined
1 (X) ( (X)
(X) - 110
k= 1.....
--
(45)
h (X)= = (X) - (r)
X-
As long as { (p()) Ja-1 k-1
follow the protocol and compute their polynomials using the correct Lagrange basis, their witnesses can be combined without introducing new random numbers, allowing the verifier to validate them together. To ensure proper use of the specified Lagrange basis, the verifier defines the vector [% A==1" where % (0, O, 1..., 1, 0, ...., 0) with
(2)
qn-1 ones spanning form (1) would require all {P()) k=1
s [[k - 1) x q"-1 + 1] to S kk x q" "I and the (g- 1) qn=1 zeros for the rest. The verifier
to demonstrate
(x) = 5f 100 (X), k == -,...... ?
(46)
where X E [1, Eq (46) is called the where (x) } is the degree q" polynomial corresponding to sh JK=1" interpolation constraints.
(0) (
Algorithm Description. All q provers preserves the wire values of the arithmetic circuits C(1)- C(2), C(3) witnesses.
Specifically, suppose the provers [p()] k=1
k= The provers first rearrange their have the witnesses {(af a (t) (()) ck
}
witnesses as (w which is the concatenated witnesses of a b and ok
(1) (1)
Suppose the order of subgroup H is q" The degrees of all the polynomials in the Plonk protocol lower than m {
(m (1n-1)). The parties agree that (p()) J k=1 non-coincident Lagrange interpolation bases. Besides, all provers will add the randomnesses when constructing the polynomials to guarantee the property for knowlene.ee
a 19 (x) (br1 bu2) (x(
(x
6 (X) (bua + bur) (X)
Wk(++1) (10
(47)
80 (X) (x) (bis + bb6) ZH
Σ (1400-))) (X),
where Wki, i E [1, 6] are the random numbers chosen by ((t) These randomness are used to mask the secret polynomials of each prover. Z (X) is the zero polynomial, which is defined as Z (X) = X" 1. And H the multiplicative subgroup with w as the th root of unity and the generator of the subgroup, such that H = [1,w,..,,""
The verifier chooses B, _ all of the provers (B, 7 Fp denotes random sampling, where a and B are uniformly
17

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
selected from the finite field P of prime order p.), who will compute the permutation polynomial { {2 (x)]
follows:
k=1
2k (X) (bu7 + bks + bxo) : (x) + + (1xx-- (X)
1 IIII WM(n++)) + BE + 7
2
(48)
1141+(1-1)x00 (X)
h: = 1,.., 9,
j=1 Wk(n(+)) + (Or - j) +
where &o is defined to be O and bay, A E [1. E [7,9) are randomnesses chosen by P!l). { (0) (X)
[x)] }
k=1 are represented
as permutation polynomials to encode permutation constraints in thPlonk constraint system. (+)) is permutation defined on the field H' of size [3n)], such that o [3r] + [3n] The field HH' defined as IH'' == H (&1 HD) (E2 H), where
$1 are chosen such that H. H. and 62- H are distinct cosets of H. ensuring H' contains 3r) distinct elements.
After computing the permutation polynomials, {P(²) [9] assign them individually. The verifier selects aF and sends it
k=1
to the provers, who then compute the quotient polynomials (x)
km1
4 (x) [x (1=) 1)-11)(1-))))0----1111---------1
(X)a -1- a (1) (X) 5 (X))a
+ 19 (X)bbe (X) (X))M + (x) (X)x xx
(((a (X) + 3X + +7) + 862 ( + +38 + +))= (0)
(49)
azt (XW) (4) (X) (1) (X) 28, +7)
(x) + BS. (X) +7) 9c)
k k= 1,...
p! splits the quotient polynomial & (X) into two polynomials, (X) and 1 (X), each degree less than v,, and a
polynomial ( ja (X) of degree at most 1) 5:
(x) = (1) (X) + X". (
(X) + x 10 (x), k= 1,....
(50)
The provers mask these polynomials with random integers baio; bal E Fp, and define:
( (x)= (X) + X",
(1) (X)(()) = 100) (X) - -b10 + bb11
(51)
Li (X): = +100" (X) - .aII+ k k= 1......
Note that:
(x) = (t) (X) + X (() (X) + X (X).
(52)
The verifier selects the evaluation challenge 2 Ep Coe Koyk, Books Es. Tkws and the linearized polynomial r/(²) (X), such that:
_ and sends the provers, who computes the open evaluations ag. by.
rk (I(X) ān (1-
# (4) (z)²²
+ +(āx + B42 + (b + 34G + (cx + Bakizz + (X))
(53)
-(āā, + (B5,0t + (b, + 8180,k + 7 (7)))2242220 So, (X))a
+ axbbgM(()) + (x) + b (X) + 2440 (X) + 96 (X), 1,....
All of the provers compute the openings of ( (x) ] }
at the evaluation challenge point z.
k=1
18

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
The verifier selects challenge 2r and sends it to the provers, who can calculate the opening polynomials W (X),
w
(X) and commit to them: k(>w)
10 (X) + olok (x) (X) @x) + 22
(1) (x) - b&)
(X)
(ck (X) ox) (So (X) 8ork)
(8)
(54)
X -
(5 k0
(1) (X) - 3km
w-(t) k(sw)
(X)
2
k = 1, ..., q.
(55)
=w
[w/(e]
[W (x) and [ [k(cw))
(x)]
The commitments the above polynomials denoted
k(sw)
{ (1)
Finally, the verifier will receive proofs
{ [
k=1
(1)
(1)
(0)
(0)
10
(10 1: ( - -,,,.
Mi
(t)
( k(sw)
ak. bk. Ck. ko Books Zkw
kmmi
(56)
k=
D.4. Batch Verification
The verifier waits for all proof' to be provided and then performs batch validation. Specifically, the verifier first preprocesses all public information defined by the arithmetic circuit, which is independent of both the witnesses and the public input w. Additionally, the verifier computes polynomial commitments for the interpolation constraints to ensure that each prover uses distinct set of Lagrange bases:
[ - [ (1- [4]
(57)
Upon receiving proofs { [_) the proof': "E k=
from each prover, the verifier combines all witnesses and then verifies the correctness of
9
(t())
(0)
[10)
9
(1)
0k
(1)
(9)
(58)
(9)
Rmi
k=1
(00)
[00
kui
k=1
[x)
(r(() (x)
k=1
(x)
Σ
(
k(zw)
(x)
1
b == 1=1,...
19

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Besides, the verifier combines the evaluation values:
k=1
bkg
9
CR1
(59)
Tk
k=1
Lke
k=1
9
Bw/ks
1..,..
k=1
Then the verifier computes the partial opening commitment
[D]
9
| V.
+
(60)
k=1
Note that in Eq (60), there are commitments for interpolating constrained polynomials, integrated by powers of a random challenge a sent by the verifier. Based the opening commitment, the verifier then computes the batch polynomial commitment [F(*], and the batch evaluation [E(]] 1:
p()) = [[D(0]
+ u. (1)
+ u (500) + v². [c(0], + u4 [801] + u (9
(61)
[X()) = (-ro + vā + 20 + v + 80, + 0880, + uzw) [1]1-
Finally, the verifier checks the equality of the following bilinear paring:
e( ([[w!))
+ u [w [=))) [w!"] + uaw W + [ - [1'0) (1]2)
(62)
Bq (62) holds, the verifier accepts batch proofs from the provers; otherwise, the verifier rejects the protocol and aborts.
D.5. Security Analysis
Theorem D.1. The zkCor* scheme, as described in Figure 3 and Subsections D.I, D.2, D.3 and D.4, is zero-knowledge scheme for FedClean.
Proof. To demonstrate that zkCor* is zero-knowledge for FedClean, we address three key properties: completeness, soundness, and zero-knowledge.
Completeness. The verification process outputs if the |Dil. De and D + |D are correctly computed by the clients, as per Eqs (39) (40) (41), and their committed datasets. The completeness of z follows from the correctness of the batch ZKP protocol we propose, which straightforward validate.
(C(): C(2), C(3)} represent the arithmetic circuit defined in Subsections D.1, D.2, D.3 and D.4. By the extractability of the polynomial commitment scheme used in the batch ZKP protocol, there exists extractor ip that, given the commitment cm, extracts witness w' = (D*, aux) such that cm zkCor .Co D" pp) with overwhelming probability, where aua represents the auxiliary witnesses generated during computation. If cm = zkCor* zkCorr.Comm D* " .r,p))
Soundness. Let C
and * V(T, cm, C, value, pp) = (value=|Dg), IDR incorrect, two scenarios can arise:
D
+ D
reps. C = C(1), C(2), C(3).), but value is
20

---

LABEL NOISE CORRECTION FOR FEDERATED LEARNING
Scenario 1:
(D", auu) satisfies C. There three possibilities:
(i) D not the committed dataset but passes the commitment verification. This is negligible due to the soundness of the polynomial commitment scheme (Kate al., 2010).
(ii) value is incorrect but passes the batch ZKP verification. This negligible in due to the soundness of the ZKP scheme. The concatenation of commitments and allocation of distinct Lagrange bases does not compromise soundness (Gabizon et al., 2019).
(iii) Some witnesses aur are incorrect but satisfy the circuits C. This negligible in À for the same reason as in (ii).
Scenario 2: to = (D, our) does not satisfy C. The soundness the proposed batch ZKP ensures that the probability of client generating a simulated proof that causes the aggregator to accept is negligible in A.
Zero-Knowledge. The zero-knowledge property follows from the characteristics of PLONK (Gabizon al., 2019). The distribution of the Lagrange bases does not affect the original eero-knowledge property in the Plonk protocol.
21


Reviews:
[{"review_id": "bdq44Z6CL3", "reviewer": "Reviewer_1B4p", "summary": "The authors proposed the method to achieve the federated learning that may involve the label noises. The proposed method, FedClean first uses the local centralized noisy label learning that selects clean samples to train the global model. Afterwards, the two-stage correct scheme is performed by exploiting local noisy label learning and global model. Finally, the model aggregation method is applied to reduce the impact of the label noises. Experimental results the improved results involving the proposed method.\n\n## update after rebuttal\n\nAfter reading rebuttals and other reviews, I became more convinced on the paper and increased my rating from weak accept to accept.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer’s time and constructive feedback on our work. Your insightful comments have helped us better clarify the contributions and limitations of our method. Below, we address each point raised in the review, and we hope our responses will alleviate your concerns.\n\n**1. Response to Methods and Evaluation Criteria**\n\n*Reviewer Comment:*\n\n\"The improvement by Mixup module is rather tiny.  I am not sure why this scheme is especially involved in the final model.\"\n\n*Response:*\n\nThank you for your valuable observation. We appreciate your attention to this detail and have carefully considered your feedback. We would like to address your concern from both theoretical and experimental perspectives:\n\n**Theoretical Considerations:**\nMixup generates new samples through linear interpolation, and its label smoothing property helps mitigate noisy labels’ impact on local models. In federated settings, where local data is limited and the noise distribution is unknown, Mixup enhances training robustness through implicit regularization. Research on both CNLL (Zhang et al. 2018) and FNLL (Xu et al., 2022) shows Mixup’s effectiveness in improving robustness against noisy labels.\n\n**Experimental Observations:**\nThe results of \"Ours w/o Mixup\" in Table 5 show that Mixup's improvement on CIFAR-10 is limited, primarily due to the dataset characteristics (50,000 samples, 10 categories) and the experimental design. The redundancy in CIFAR-10 reduces Mixup’s impact, and to ensure comparability with previous works (e.g., FedNoRo), we used their experimental setup, which might not fully highlight Mixup’s potential with smaller client data.\n\nAdditionally, Mixup’s simple implementation and low time/space complexity make it a valuable addition regardless of its exact performance improvement.\n\n[1] Zhang, H., Cisse, M., Dauphin,Y.N., and Lopez-Paz, D. mixup: Beyond empirical risk minimization. In International Conference on Learning Representations, 2018.\n\n[2] Xu, J., Chen, Z., Quek, T.Q., and Chong, K.F.E. Fedcorr: Multi-stage federated learning for label noise correction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,pp.10184–10193,2022.\n\n**2. Response to Methods and Evaluation Criteria**\n\n*Reviewer Comment:*\n\n\"In Table 5, 'Ours w/o correction' already outperforms others in Table 2... raises questions on which part contributes most to SOTA performance.\"\n\n*Response:*\n\nThank you for your insightful observation. We greatly appreciate your attention to the details of our results. We address your concerns as follows:\n\nOur method consists of two main components (Figure 1). The first part is the preprocessing stage, involving CNLL conducted locally by clients. However, due to limited client dataset sizes in FL, particularly with non-IID data, relying solely on CNLL methods does not achieve ideal training performance. Therefore, existing FNLL methods, including FedFixer, discard CNLL and design FNLL methods relying on clean clients.\n\nOur research has shown that while CNLL cannot be directly applied to FL, when integrated into our two-stage correction mechanism, FedClean performs better than most state-of-the-art methods with the existation of clean clients (a scenario widely studied). In this case, FedClean's performance was slightly lower than the best method by only 2\\%. However, in the absence of clean clients (a scenario often neglected), existing FNLL methods fail, while FedClean still performs robustly.\n\nWe acknowledge that FedClean's success relies substantially on the preprocessing module (particularly CNLL, as evidenced by the 'Ours w/o CNLL' results in Table 5). However, without integration with the two-stage correction module, the CNLL alone cannot simultaneously achieve: (1) robust performance regardless of clean clients' presence, and (2) the capability to fill the research gap where existing FNLL methods fail to consider scenarios without clean clients.\n\nRegarding your concern: “I suspect the model could surpass others due to a good baseline rather than the proposed method,” we would like to highlight that Table 2 clearly shows that when all clients are noisy, existing methods fail, while FedClean maintains stable performance. Compared to other FNLL methods, FedClean achieves at least a 20\\% improvement.\n\nWe hope this clarifies the unique contribution of our method and reassures you of our approach's validity. Thank you again for your thoughtful feedback, which helped refine our explanation.", "claims_and_evidence": "- The proposed method seems especially effective in the situation where most of clients involves label noises. This seems a good characteristic of the proposed method.", "methods_and_evaluation_criteria": "The improvement by Mixup module is rather tiny. I am not sure why this scheme is especially involved in the final model.", "theoretical_claims": "The theoretical proof looks correct.", "experimental_designs_or_analyses": "- In Table 5, I found that \"Ours w/o correction\" is already far better than other methods in Table 2. This raises questions on which part makes the method achieve the SOTA performance: I suspect that the model could surpass other methods thanks to the good baseline rather than the proposed method. Espeically, the gap between the full model (Ours) and Ours w/o correction is only 7-10% in IID and non-IID cases, respectively in Table 5. However, the gap between Ours w/o correction in Table 5 and best performed FedFixer in Table 2 is is over 12%.", "relation_to_broader_scientific_literature": "The method could be useful in the context of both FL and CL.", "essential_references_not_discussed": "I think reference are rather complete.", "other_strengths_and_weaknesses": "None", "other_comments_or_suggestions": "None", "questions_for_authors": "None", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_flag": "Flag this paper for an ethics review."}}, {"review_id": "Z06U6TN9pj", "reviewer": "Reviewer_Wzch", "summary": "The paper proposes FedClean, a robust label noise correction framework for federated learning that addresses client-side label noise without assuming clean clients or specific noise distributions. Key contributions include: (1) Two-stage label correction. Combines local centralised noisy label learning to select clean samples and global model insights to rectify noisy labels, aided by a collaborative per-sample loss to reduce false corrections. (2) Adaptive aggregation. Adjusts client influence in global model updates based on clean sample sizes, mitigating noise impact while incorporating secure label correction (zkCor). (3) Versatility and performance. Demonstrates effectiveness in high-noise scenarios (including 100% noisy clients) through experiments on synthetic and real-world datasets, outperforming existing FL noise-label learning methods. FedClean enhances global model robustness by jointly leveraging local noise correction and global model confidence.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for their thoughtful feedback and constructive suggestions. Below, we address the raised points:\n\n**1. Applicability to Other Data Types**\n\nFedClean's design is fundamentally modality-agnostic, as its core components - the two-stage label correction and adaptive aggregation (ASSA) - operate on general principles of prediction confidence and sample consistency rather than domain-specific features. While our current validation used image datasets for benchmarking, the framework's reliance on comparing annotation labels with model predictions (Eq. 11) makes it directly applicable to text data, and ASSA's dynamic client weighting (Eqs. 4,15,18) naturally extends to time-series or other sequential data. The methodology requires no architectural changes for different data types, only standard modality-specific feature extractors. We recognize the importance of empirical validation across domains and will include comprehensive non-image experiments (particularly for NLP and time-series) in future work to further demonstrate this versatility.\n\n**2. Complexity and Hyperparameters**\n\nWhile FedClean introduces three key hyperparameters ($\\sigma_1$, $\\sigma_2$ for correction rates and $\\epsilon$ for confidence threshold), our extensive experiments demonstrate remarkable robustness to their variations - for instance, varying $\\sigma_1$ by $\\pm 0.1$ caused less than 1\\% accuracy fluctuation on CIFAR-10. The two-stage architecture was deliberately designed to minimize sensitivity to parameter choices while maintaining effectiveness. To further assist practitioners, we will provide detailed guidelines for parameter selection based on noise level estimation.\n\n**3. Extension to Semi-Supervised/Unsupervised FL**\n\nWe agree with the reviewer's suggestion about extending FedClean to semi-supervised and unsupervised FL settings, as our framework's core label correction mechanism (Section 3.4) is particularly well-suited for such scenarios. The two-stage correction approach can naturally incorporate pseudo-labeling for unannotated data by leveraging the global model's predictions as high-confidence labels, while the adaptive aggregation (ASSA) would maintain robustness against potentially noisy pseudo-labels. Furthermore, the framework's architecture provides a natural pathway for unsupervised adaptation through integration of contrastive learning or other clustering techniques to discover latent label structures from unlabeled data. These promising extensions align perfectly with our future work plans (Section 6) and could significantly broaden FedClean's applicability to real-world scenarios where labeled data is scarce. \n\n**4. Generalizability Validation**\n\nWe agree that testing on diverse data types strengthens FedClean’s claims. To address this, in future work, we will further study experiments on text (e.g., AG News) and time-series (e.g., UCI HAR) datasets.  \n\nWe appreciate the reviewer’s insightful comments, which highlight valuable extensions for FedClean. Our responses clarify the framework’s versatility and outline concrete steps for future work. We are committed to refining FedClean’s applicability across domains and learning paradigms.", "claims_and_evidence": "Yes, all claims made in the submission are well supported.", "methods_and_evaluation_criteria": "The proposed methods are well-motivated for tackling label noise in federated learning. The integration of CNLL, Mixup, and ASSA directly addresses challenges such as identifying clean samples and mitigating the influence of noisy clients. Moreover, the evaluation criteria—using benchmark datasets like CIFAR-10, CIFAR-100, and Clothing1M under both IID and non-IID settings with varying noise ratios—are appropriate for demonstrating the method's robustness and practical relevance. However, further evaluations in larger-scale or more diverse real-world scenarios could provide additional insights into the approach’s scalability and generalisability.", "theoretical_claims": "I examined the proofs for Theorem 3.1 and Theorem 3.2. Theorem 3.1 establishes a bound-on performance improvement when incorporating inferred labels via a MAP estimation approach, while Theorem 3.2 uses a Bayesian argument to demonstrate that inferred labels offer additional stability in estimating true labels. The logical flow of both proofs is generally sound and leverages standard probabilistic tools such as Bayes’ theorem. The two theorems align with practical FL challenges, addressing label noise through a blend of Bayesian reasoning and collaborative learning, and the reliance on appendices for extended proofs indicates thorough theoretical exploration beyond the main text. These aspects underscore the conceptual rigour of FedClean’s theoretical claims, supporting its innovation in federated label noise correction.", "experimental_designs_or_analyses": "The experimental design and analyses demonstrate strong validity and soundness, with several notable strengths: FedClean is rigorously evaluated against two baselines and five state-of-the-art methods, ensuring a thorough benchmark, while experiments cover both IID (CIFAR-10/100) and non-IID (CIFAR-10, Clothing1M) data distributions, as well as varying noise levels and noise bounds, showcasing FedClean’s adaptability. The use of Clothing1M, a dataset with inherent label noise, validates FedClean’s robustness in practical, noisy environments, and the inclusion of ablation studies highlights the contributions of individual components (e.g., CNLL methods like Co-teaching and Joint Optim) to FedClean’s performance. Notably, FedClean maintains strong performance even when all clients are noisy, demonstrating its independence from clean clients and superior noise resilience.", "relation_to_broader_scientific_literature": "The paper’s key contributions address gaps in federated learning (FL) and noisy label learning. Previous FL work highlights the challenge of label noise in decentralized settings, which degrades model performance due to the lack of centralized data preprocessing. While methods like Co-teaching and DivideMix work well in centralized settings, they fail in FL because of privacy issues and limited local data diversity. FedClean bridges this gap by adapting these techniques for FL while maintaining privacy. Unlike other methods that discard noisy clients or assume clean clients, FedClean works in scenarios where all clients may be noisy, making it suitable for real-world issues like federated crowdsourcing and adversarial attacks. It introduces a two-stage correction scheme that combines local noisy label learning and global model insights, improving robustness and reducing false corrections. FedClean’s ASSA adjusts client influence based on clean sample sizes, unlike methods like FedFixer and FedRN, which rely on specific noise distributions or increase communication overhead. It also uses zkCor for secure label correction, protecting privacy while enhancing robustness. Overall, FedClean overcomes the limitations of prior methods, offering a versatile solution for noisy label problems in FL.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths:\n1. FedClean proposes a novel two-stage label correction scheme that combines local noisy label learning with global model insights, addressing a critical gap in FL by eliminating the assumption of clean clients. This is a significant departure from existing methods which rely on clean clients.  \n2. FedClean’s ability to handle 100% noisy clients is a major advancement, making it applicable to real-world scenarios like federated crowdsourcing and adversarial environments, where clean clients may not exist.\n3. The framework’s versatility, not assuming specific noise distributions, broadens its applicability compared to methods like FedFixer, which are limited to specific noise types.\n4. The paper is well-structured, with a clear taxonomy of related works in both centralized and federated noisy label learning, providing a solid foundation for understanding the contributions.\n\n\nWeaknesses: \n1. While FedClean performs well on image datasets (e.g., CIFAR, Clothing1M), exploring its applicability to other data types (e.g., text, time-series) could further validate its generalisability.\n2. The approach involves multiple stages and numerous hyperparameters, which might require careful tuning in practical applications.", "other_comments_or_suggestions": "It would be better if FedClean could be Extended to semi-supervised or unsupervised FL scenarios, where labeled data is scarce, which could broaden its impact.", "questions_for_authors": "1. While FedClean performs well on image datasets, can it be explored the applicability to other data types to further validate its generalizability?\n2. Can FedClean be extended to semi-supervised or unsupervised FL scenarios, where labeled data is scarce, to broaden its impact in future work?\n3. Could exploring FedClean's applicability to other data types further validate its generalisability, given its success on image datasets?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "L96KoNuWxZ", "reviewer": "Reviewer_Eovt", "summary": "The paper introduces FedClean, a robust label noise correction method for federated learning that employs a two-stage correction process to identify and rectify noisy labels, coupled with adaptive sample-size-weighted aggregation. Notably, FedClean operates effectively without requiring clean clients or assumptions about specific noise distributions. Extensive experimental results show the effectiveness of the propsoed FedClean method.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the constructive feedback. Below, we address the concerns raised:\n\n**1.** **_Reviewer Comment:_** **The propsoed method seems not work well: In Table 2, the baselines beat the proposed method In addition to the setting $\\rho=1$ and $\\tau=0.5$ both on IID and Non-IID setting.**\n\nThe reviewer observes that baselines outperform FedClean when not all clients are noisy (e.g., $\\rho=0.5$). This is expected because methods like FedCorr and FedNoRo assume the existence of clean client, allowing them to leverage clean data more effectively. In contrast, FedClean is designed for scenarios where clean clients may not exist (e.g., $\\rho=1$), prioritizing robustness over specialized performance in idealized settings. While FedClean shows marginally lower accuracy when $\\rho=0.5$ (e.g., 86.79\\% vs. FedCorr’s 89.11\\% in non-IID), the difference is minimal ($\\le2.3\\%$). However, when $\\rho=1$, FedClean improves the accuracy by at least more than 20\\%. This trade-off ensures FedClean’s versatility in real-world FL deployments, where clean clients are often unavailable.\n\n**2.** **_Reviewer Comment:_** **In Table 3, the results on CIFAR-100 dataset with Non-IID setting is missing.**\n\nThe experimental results of FedClean on CIFAR-100 non-IID show consistent patterns with CIFAR-10, where FedClean outperforms baselines and state-of-the-art methods under high noise levels (e.g., FedClean$^2$ achieves 62.36\\% accuracy for $\\rho=1$, $\\tau=0.5$, compared to FedCorr's 38.64\\%). The omission of these results in Table 3 is due to two reasons:  \nPrevious FNLL studies (e.g., FedCorr(Xu et al., 2022), FedNoRo(Wu et al., 2023)) did not report CIFAR-100 non-IID results, perhaps due to their underperformance on this dataset. Due to the limited space and also following the same result presentation style of previous FNLL studies, we also did not include the CIFAR 100 results in the main body. However, following your suggestion, we will include the following table in the appendix of the final version.\n\n\nThe best accuracies (\\%) of various methods on CIFAR-100 dataset with non-IID setting at different noise levels.\n | Methods        | ρ=0, τ=0 | ρ=0.5, τ=0.3 | ρ=1, τ=0.5 |\n|----------------|:----------:|:------------:|:------------:|\n| FedAvg         |   68.75    |     58.34     |     29.12     |\n| FedProx        |   69.72    |     59.01     |     30.45     |\n| FedCorr        |   68.73    |     68.95     |     38.64     |\n| FedNoRo        |   67.84    |     65.22     |     35.78     |\n| FedBeat        |   66.91    |     63.10     |     28.93     |\n| FedELC         |   66.55    |     63.75     |     29.67     |\n| FedClean$^1$ |   67.15    |     65.83     |     60.89     |\n| FedClean$^2$ |   67.80    |     66.45     |     62.36     |\n\n**3.** **_Reviewer Comment:_** **In Table 5, we can see that Mixup seems unimportant in the proposed framework.**\n\n**Mixup is very important**, we have provided a detailed explanation in our response to **Reviewer 1B4p**’s similar question (see *''Response to Methods and Evaluation Criteria”*). We kindly refer you to that section for a comprehensive discussion.\n\n**4.** **_Reviewer Comment:_** **The communication cost is lacked.**\n\nThank you for your comment. **We would like to clarify that our study focuses on learning from noisy data in FL while not the communication cost.** We just use the standard FL protocol (FedAvg), whose communication cost can be found in FedAvg\n(McMahan et al., 2016). Our method does NOT include any additional communication overhead. Also, communication environments have NO impact on the performance of our method. Like many previous FNLL studies (e.g., FedCorr(Xu et al., 2022), FedNoRo(Wu et al., 2023), etc.) which did not mention communication issues, communication efficiency is out of the scope of our study.\n\n**5.** **_Reviewer Comment:_** **This paper's contributions are limited, as it repeatedly employs common data partitioning and data augmentation methods.**\n\nFedClean’s core novelty lies in its two-stage correction and no assumption of clean clients, addressing a critical gap in prior work. Existing methods fail when all clients are noisy. Additionally, ASSA dynamically adjusts client weights based on clean sample contributions, departing from static aggregation in FedAvg. These innovations enable FedClean to generalize across noise distributions and client configurations, improving FL robustness in practical settings.\n\nIn federated crowdsourcing, clean clients rarely exist due to the limited expertise of workers. Therefore FedClean’s ability to function without clean clients is crucial. This is a challenge existing methods fail to address. FedClean fills this gap, making FL systems more reliable in environments with highly variable data quality. Thus, we believe our contributions are significant.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "I have gone through the theorem and found no obvious errors", "experimental_designs_or_analyses": "The experimental design comprehensively evaluates FedClean against baseline and state-of-the-art methods on benchmark datasets with diverse label noise levels. Evaluations are conducted under both IID and non-IID settings, supplemented by ablation studies to assess the contributions of individual components to overall performance.", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths:\n1. This paper presents FedClean, a new and robust label noise correction framework for federated learning.\n2. The manuscript is clearly written and well-structured, ensuring ease of readability.\n3.  Extensive experiments demonstrate that FedClean consistently outperforms existing baselines across diverse noise levels.\n\nWeaknesses:\n1. The propsoed method seems not work well: In Table 2, the baselines beat the proposed method In addition to the setting $\\rho$=1 and $\\tau$=0.5 both on IID and Non-IID setting.\n2. In Table 3, the results on CIFAR-100 dataset with Non-IID setting is missing.\n3. In Table 5, we can see that Mixup seems unimportant in the proposed framework.\n4. The communication cost is lacked.", "other_comments_or_suggestions": "Please refer to the weakness", "questions_for_authors": "1. The propsoed method seems not work well: In Table 2, the baselines beat the proposed method In addition to the setting $\\rho$=1 and $\\tau$=0.5 both on IID and Non-IID setting.\n2. In Table 3, the results on CIFAR-100 dataset with Non-IID setting is missing.\n3. In Table 5, we can see that Mixup seems unimportant in the proposed framework.\n4. The communication cost is lacked.\n5. This paper's contributions are limited, as it repeatedly employs common data partitioning and data augmentation methods.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "n31LPCUuiR", "reviewer": "Reviewer_x3K4", "summary": "The paper introduces FedClean, a robust framework designed to address label noise in federated learning (FL) scenarios. FedClean employs a two-stage label correction approach to identify and rectify noisy labels from both local noisy label learning and global model perspectives. It also proposes a novel adaptive sample size-weighted aggregation (ASSA) method to mitigate the impact of label noise and enhance global model performance. FedClean does not assume the presence of clean clients or specific noise distributions, making it highly versatile. Key contributions include: (1) a two-stage label correction scheme with a collaborative per-sample loss to reduce false corrections, (2) the ASSA method to adjust client influence based on clean sample sizes, and (3) extensive experiments demonstrating that FedClean outperforms existing noise-label learning methods in FL, effectively handling label noise even when all clients are noisy.\n\n## update after rebuttal\nAfter reading the authors' responses, I decide to keep my original score.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewers for their constructive feedback.  Below are our responses to the comments and questions:\n\n**1. Basic CNLL Implementation \\& Overfitting Risk**\n\nThe reviewer raises a valid point regarding the use of basic CNLL methods.  In this work, we intentionally adopted standard CNLL techniques (e.g., Co-teaching and Joint Optim) to establish a baseline comparison and demonstrate the generality of FedClean’s framework.  While advanced CNLL methods could further improve performance, our experiments already show that FedClean outperforms state-of-the-art methods even with these \"basic\" implementations.\nImportantly, FedClean’s two-stage correction and ASSA inherently mitigate overfitting risks:\n(1) **Stage 1**: Clean sample selection via local CNLL filters out noisy data early.  \n(2) **Stage 2**:  Collaborative per-sample loss and adaptive aggregation (ASSA) dynamically adjust client contributions, reducing reliance on any single model’s predictions. \n(3) **Mixup**: Label smoothing further enhances robustness.\n\nWe acknowledge that integrating more sophisticated CNLL methods (e.g., DivideMix) is a promising direction. Preliminary tests with DivideMix showed a 1.2–2.5\\% accuracy gain on CIFAR-10, validating this potential. However, we prioritized simplicity to highlight FedClean’s core contributions. Future work will explore this in depth.\n\n**2. Assumption on Clothing1M Dataset**\n\nThe Clothing1M dataset inherently contains real-world label noise (about 40\\% noise rate), and prior work treats all clients as noisy by default. Our setup reflects this property to evaluate FedClean’s ability to handle pervasive noise.\n\nThat said, FedClean does not strictly assume uniform noise levels across clients. The framework naturally accommodates variability:\n(1) **ASSA**: Clients with fewer clean samples (higher noise) contribute less to aggregation (Eq. 4, 15, 18).\n(2) **Collaborative loss**: Adjusts correction confidence per sample (Eq. 11), adapting to local noise conditions.\nIn future work, we will explicitly test FedClean on datasets with heterogeneous client noise levels (e.g., varying $\\tau$ per client) to further validate its flexibility.\n\n**3. Unifying Theorem Numbering**\n\nWe appreciate the reviewer’s observation. The discrepancy arises because Theorems 3.1 and 3.2 in the main text correspond to Theorems B.1 and C.1 in the appendix. This was an oversight in numbering due to the appendix’s structure. We will revise the appendix to align theorem labels (e.g., \"Theorem 3.1 (Extended Proof)\") for clarity.\n\nWe thank the reviewers for their insightful feedback, which strengthens our work. We will incorporate all suggestions, including theorem numbering fixes and expanded experiments on heterogeneous noise, in the final version. FedClean’s design principles—eliminating restrictive assumptions and leveraging collaborative correction—provide a robust foundation for real-world FL deployments, and we are excited to build upon this framework.", "claims_and_evidence": "Yes, all claims made in the submission are supported by clear and convincing evidence.", "methods_and_evaluation_criteria": "Yes, both proposed methods and evaluation criteria make sense for the problem and application.", "theoretical_claims": "Yes, both proofs for the theoretical claims are correct, with a sound overall structure and logical flow. The proofs employ standard techniques in Bayesian inference, maximum a posteriori estimation, and error analysis. The arguments for incorporating inferred labels to improve model accuracy and provide stability in noisy environments are well-supported by the mathematical framework. In Theorem 3.1, the use of Bayes' theorem to update model predictions with inferred labels as prior information is correctly applied, and the MAP estimation to refine predictions is consistent with Bayesian learning principles. The error analysis compares misclassification rates before and after incorporating inferred labels, showing a valid performance improvement. In Theorem 3.2, Bayes' theorem is used again to compute the posterior probability, correctly combining the model's prediction and the inferred label. The proof demonstrates that inferred labels provide a stable signal even in the presence of noisy annotations, enhancing the robustness of the model, and it effectively shows how inferred labels act as stabilizing factors, leading to more accurate final estimates.", "experimental_designs_or_analyses": "Yes, the experimental design in this paper appears solid, with a clear comparison to state-of-the-art methods across multiple settings, including both IID and non-IID distributions. The use of multiple benchmark datasets (CIFAR-10, CIFAR-100, and Clothing1M) with varying levels of label noise is appropriate for assessing robustness to noisy clients. Additionally, the inclusion of ablation studies (FedClean variants) allows for insight into the effectiveness of specific features.", "relation_to_broader_scientific_literature": "FedClean's key contributions extend prior research in FL and noisy label correction, offering novel solutions to improve effectiveness and robustness. The paper highlights the importance of FL in privacy-sensitive domains, focusing on challenges from label noise and the limitations of centralized learning due to privacy concerns and data diversity. FedClean's noise correction framework contrasts with methods assuming clean clients or specific noise distributions, addressing real-world FL challenges. The two-stage label correction scheme and per-sample loss assessment method advance FL noise correction. The Adaptive Sample Size-Weighted Aggregation (ASSA) method adjusts client influence to reduce label noise and improve model performance, while zkCor enhances privacy. Experimental results validate FedClean’s superior performance, establishing it as a more effective solution compared to existing FL methods.", "essential_references_not_discussed": "No, there are no essential references not discussed.", "other_strengths_and_weaknesses": "Strengths:\n1. Originality and Novelty: The paper's approach is quite original, particularly in how it removes restrictive assumptions made by prior work. Most existing methods for label noise correction in federated learning (FL) assume the existence of clean clients or specific noise distributions. In contrast, the proposed FedClean framework does not make these assumptions, which significantly broadens its applicability in real-world scenarios where all clients could be noisy. The introduction of the two-stage label correction scheme and adaptive sample size-weighted aggregation (ASSA) method is a creative combination of existing ideas, offering a robust solution to the challenge of noisy data in federated settings.\n2. Practical Significance: The framework’s application to federated learning (FL) is of high significance given the growing use of FL in privacy-sensitive domains such as healthcare, finance, and IoT. The ability to handle label noise in FL models without compromising client privacy opens up significant opportunities for more robust, real-world machine learning deployments.       This work addresses a critical gap in the literature and directly contributes to improving the practical effectiveness of FL models in real-world applications.\n3. Clear and Well-Structured Presentation: The paper is clear and well-structured, with a logical flow from introduction to problem definition, related work, the proposed method, and experimental validation. The explanation of key concepts, such as label noise in FL and existing methods for noise correction, is straightforward and accessible. The contribution of the FedClean framework is clearly articulated, making it easy for the reader to grasp the innovation and advantages over existing approaches.\n4. Experimental Validation: The experimental results provided are thorough and demonstrate the efficacy of the proposed method. Testing on synthetic and real-world datasets allows the paper to show that FedClean not only performs well but also outperforms state-of-the-art methods in mitigating label noise, even when all clients are noisy. This adds credibility and reliability to the claims made in the paper.\n\nWeaknesses:\n1.It’s important to consider the implementation of CNNL methods, which are noted as relatively basic in this work—future improvements in CNNL could enhance the method's performance, but it's not clear whether these enhancements have been practically tested or if there's a risk of overfitting to the baseline.\n2.One potential concern is the assumption that all clients in the Clothing1M dataset are noisy, as this may oversimplify the real-world variability in client noise levels.", "other_comments_or_suggestions": "Since Appendix.B and Appendix.C in the supplementary materials are supplementary proofs of Theorem 3.1 and Theorem 3.2 in the main text,Then it shows that Theorem B.1 and Theorem C.1 mentioned in the supplementary material are Theorem 3.1 and Theorem 3.2 respectively, so why not unify them?", "questions_for_authors": "1.It’s important to consider the implementation of CNNL methods, which are noted as relatively basic in this work—future improvements in CNNL could enhance the method's performance, but it's not clear whether these enhancements have been practically tested or if there's a risk of overfitting to the baseline.\n2.One potential concern is the assumption that all clients in the Clothing1M dataset are noisy, as this may oversimplify the real-world variability in client noise levels.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper addresses the problem of general robust label noise correction in federated learning. The authors propose a novel framework named FedClean, which integrates local centralized noisy label learning, a two-stage correction scheme, and an effective model aggregation strategy. Experimental results on several datasets demonstrate the efficacy of the proposed approach.


Strengths:
1. The proposed method is particularly effective in scenarios where the majority of clients contain noisy labels.
2. FedClean is both novel and versatile, offering a general framework for robust federated learning under label noise.
3. The paper is well-organized and clearly written, making it easy to follow.
4. Extensive experiments show that FedClean outperforms existing baselines across various noise levels.


Weaknesses:
1. The experimental analysis could be strengthened, particularly for Tables 2, 3, and 5, where more detailed insights would help substantiate the findings.
2. One potential limitation lies in the assumption that all clients in the Clothing1M dataset are noisy. This may oversimplify real-world scenarios where client noise levels often vary significantly.



Overall, the paper presents a promising and well-structured approach to label noise correction in federated learning. For the camera-ready version, I suggest the authors enhance the analysis of experimental results to provide deeper insights. Additionally, exploring more realistic and general settings--such as those involving heterogeneous noise distributions across clients--would further strengthen the paper’s contributions and practical relevance.
Decision: Accept (poster)