Federated Learning with Openset Noisy Labels
Authors: Zonglin Di
Zhaowei Zhu
Xin Eric Wang
Yang Liu
Venue: Submitted to ICLR 2023 (2023)
Keywords: Federated Learning
OpenSet Classification
Noisy Label

Abstract: Federated learning is a learning paradigm that allows the central server to learn from different data sources while keeping the data private at local. Without controlling and monitoring the local data collection process, it is highly likely that the locally available training labels are noisy, just as in a centralized data collection effort. Moreover, different clients may hold samples within different label spaces. The noisy label space is likely to be different from the unobservable clean label space, resulting in openset noisy labels. In this work, we study the challenge of federated learning from clients with openset noisy labels. We observe that many existing solutions, e.g., loss correction, in the noisy label literature cannot achieve their originally claimed effect in local training. A central contribution of this work is to propose an approach that communicates globally randomly selected ``contrastive labels" among clients to prevent local models from memorizing the openset noise patterns individually. Randomized label generations are applied during label sharing to facilitate access to the contrastive labels while ensuring differential privacy (DP). Both the DP guarantee and the effectiveness of our approach are theoretically guaranteed. Compared with several baseline methods, our solution shows its efficiency in several public benchmarks and real-world datasets under different noise ratios and noise models. 

Under review as a conference paper at ICLR 2023
FEDERATED LEARNING WITH OPENSET NOISY LA- BELS
Anonymous authors Paper under double-blind review
ABSTRACT
Federated learning learning paradigm that allows the central server to learn from different data sources while keeping the data private at local. Without con- trolling and monitoring the local data collection process, it is highly likely that the locally available training labels are noisy, just as in a centralized data collection effort. Moreover, different clients may hold samples within different label spaces, The noisy label space is likely to be different from the unobservable clean label space, resulting in openset noisy labels. In this work, we study the challenge federated learning from clients with openset noisy labels. We observe that many existing solutions, e.g., loss correction, in the noisy label literature cannot achieve their originally claimed effect in local training. A central contribution of this work is to propose an approach that communicates globally randomly selected "contrastive labels" among clients to prevent local models from memorizing the openset noise patterns individually. Randomized label generations are applied during label sharing to facilitate access to the contrastive labels while ensuring differential privacy (DP). Both the DP guarantee and the effectiveness of our ap- proach are theoretically guaranteed. Compared with several baseline methods, our solution shows its efficiency in several public benchmarks and real-world datasets under different noise ratios and noise models.
INTRODUCTION
With the development of distributed computation, federated learning (FL) emerges as a powerful learning paradigm for its ability to train with data from multiple clients with strong data privacy protection (McMahan et al. 2017 Kairouz et al. 202T Yang al., 2019). With each of the dis- tributed clients having different collection and annotation process, their observed data distributions are likely be highly heterogeneous and noisy. This paper aims provide solutions for a practical FL setting where not only do each client's training labels carry different noise rates, the observed label space at these clients will differ as well, even though their underlying clean labels are drawn from the same label space. For example, in global medical system, the causes (labels) of discase are annotated and reported by doctors, and these labels are potentially noisy due to the differences in the doctors' training backgrounds (Ng et al. 2021). When certain causes and cases only be found in data clients from country A but not country B, the observed noisy label classes country A will then differ from the one of country B. We call such a federated learning system has openset noise problems if the observed label space differs across clients.
We observe that the above openset label noise will pose significant challenges if apply the ex- isting learning with noisy labels solutions locally at each client. For instance, a good number of these existing solutions operate with centralized training data and rely on the design of robust loss functions (Natarajan et al. 2013 Patrini et al.) 2017 Ghosh et al.) |017 [Zhang & Sabuncu] 2018)) Feng et al. 22021] Wei & Liu 2021, 2 Zhu et al. (021a). Implementing these approaches often re- quires assumptions, which are likely to be violated if we directly employ these centralized solutions
in federated learning setting. For example, loss correction a popular design of robust loss func-
tions (Patrini et al.) 2017 Natarajan et al. 2013 Liu & Tao 2015 Scott 2015 Jiang et al. 2022). where the key step is to estimate the label noise transition matrix correctly (Bae et al. 2022 Zhang 2021b 2022). Correctly estimating the label noise transition matrix
et al.) 2021b Zhu et al.
quires observing the full label space, when the ground-truth labels are not available. In FL where the transition matrix is often estimated only with the local openset noisy labels, existing estimators

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of the noise transition matrix would fail. Moreover, even though we can have the best estimate of the noise transition matrix as if we have the ground-truth labels for the local instances, the missing of some label classes would make the estimate different from the ground-truth one, and again leads to failures (detailed example in Section 3.2
Given the difficulties in estimating the noise transition matrix, we develop new solution FedPeer to tackle the challenge of learning from openset noisy labels in FL. Our solution is inspired by the idea of using "contrastive labels", whose implementation does not require the knowledge of the noise transition matrix. Notable examples of contrastive labels in the learning with noisy label communities include negative labels (Kim et al., 2019 Wei et al.. 2022a), peer labels (Liu & Guoj 2020), and complementary labels (Ishiguro et al. 20022 Feng et al. 2222)]. The high-level idea is to introduce a negative loss using contrastive labels to punish a model for overfitting to the noisy label distributions. Nonetheless, applications of these approaches would require sampling of global "ccottrattive" noisy labels constructing local contrastive labels in each client will be problematic again since different clients may have different noisy label spaces in the openset noise setting. Our solution FedPeer has an explicit step to communicate labels among clients in a differentially private Dwork 2008
Dwork et al. 2014) way.
Our contributions are summarized as follows.
We formally define the openset noise problem in FL, which is more practical than the existing homogeneous noisy label assumptions. The challenges along with the openset noise are also motivated by analyzing the failure cases of the existing popular noisy learning solutions such as loss correction (Natarajan et al., 2013 Patrini et al. 2017 Liu & Tao 2015).
We propose a novel framework, FedPeer, to solve the openset label noise problem. FedPeer builds on the idea of contrastive labels, and adopts peer loss (Liu & Guo (020) as a building block.
To mitigate the gap between centralized usage of contrastive labels and the federated one, we propose a label communication algorithm with a differential privacy (DP) guarantee. We also prove that benefiting from label communication, the gradient update of aggregating local peer loss with FedAvg is guaranteed to be the same as the centralized implementation of peer loss, therefore establishing its robustness to label noise.
We empirically compare FedPeer with several baseline methods on both benchmark datasets and practical scenarios, showing that, in terms of FL with openset label noise, directly applying cen- tralized solutions locally cannot work and FedPeer significantly improves the performance.
2 RELATED WORKS
Federated learning is a collaborative training method to make full use of data from every client without sharing the data. FedSGD (Shokri & Shmatikov| (2013 is the way of FL pass the gradient between the server and the clients. To improve the performance, FedAvg (McMahan et al., 2017) is proposed and the model weight is passed between the server and the clients. In practice, openset problem is common in FL because the source of every client may vary a lot and it is very likely to find that some of the classes are unique in the specific clients. There are a lot of works to analyze and solve the non-Ill) problem in FI. (Zhao et al., 2018 Li et al. 2019 2021
Zhang et al. 2021a
[i et al. 20205 Karimireddy et al. 2020) Andreux et al. 2020).
Label noise is common in the real world (Agarwal et al. 2016
Xiao al.) 2015 Zhang et al.,
2017 Wei et al. 2022b). Traditional works on noisy labels usually assume the label noise is class- dependent, where the noise transition probability from clean class to noisy class only depends on the label class. There are many statistically guaranteed solutions based on this assumption (Natarajan
al., 2013 Menon et al. 2015 Liu & Tao 20155] Liu & Guo 2020). However, this assumption fails to model the situation w where different group of data has different noise patterns (Wang et al. [02T))
For example, different clients are likely to have different noisy label spaces, resulting totally different underlying noise transitions. Existing works on federated learning with noisy labels mainly assume
the noisy label spaces are identical across different clients (Yang et al., 2022 Xu et al. (022). There are other notable centralized solutions relying on the memorization effect of large model (e.g..
deep neural network) (Li et al. 20200a Liu 2021 1 Song et al. 2019 Xia et al. 2021 Liu et al. 2020 Cheng et al. 2020). However, in a federated learning system, simply relying on the memorization
effect would fail, i..., the model can perfectly memorize all local noisy samples during local training,
since the local data is likely to be imbalanced and with limited amount (Han et al. 2020
Liu 2021
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The idea of contrastive labels is punish the overfitting, which is supposed avoid memorizing openset local noisy samples. Besides, the concept "openset" is also used in Tuor et al. (2021). where the focus is on the out-of-distre -ii-tribbiiiitoionn features and their labels are called openset noise. it different from ours since they did not focus on in-distribution mislabeled data.
3 FORMULATIONS AND MOTIVATIONS
We first formulate the federated learning problem with noisy labels (Section 3.1 then formally define the openset label noise in FL and motivate the necessity of our approach in this setting by showing examples of failures in existing methods (Section 3.2.
3.1 FEDERATED LEARNING WITH NOISY LABELS
Federated learning Consider K class classification problem in federated learning system with C' elients. Each client E [C] ;= {1, ... , CC} holds a local dataset D. := {( (a).snn))eee hel]N)) where N₂ is the number of instances in De and N₂ == fl.... .Ne.. Assume there no overlap among De, Vc. Denote the union of all the local datasets by D := {(I,, yn hng]]- Clearly, == -=E Eeejey Ne. Denote by D. the local data distribution, De the local random variables of feature and label, DD the global/centralized data distribution, and (X.Y) ~ 'D the corresponding global random variables. Denote by X, Xe V. and Ve the space of X, Xx, Y, and Y,, respectively. FL builds the following distributed optimization problem: ming Eee(c] N Lo)(),, where f is the classifier, 0 is the parameter of f. To this end, the local training and global model average executed iteratively. In local training, each client learns model fc X' + y with its local dataset De by minimizing the empirical loss (.(()) defined (0.) N. Ene]N.] (1%, 0.). vG),, where for classification problems, the loss function is usually the cross-entropy (CE) loss: {(f(x; Y) = (f(x;e) [Y]).
have D De and N =
(YT). Y [K]. In the following
global model average, each client sends its model parameter 0g to the central server, which is further aggregated following FedAvg (McMahan et al. 2017):
No
0g-
(1)
N
CE|C]
3.2 OPENSET NOISE IN FEDERATED LEARNING
When the label y is corrupted, the clean dataset D becomes the noisy dataset === [Iin Un heEN where In is the noisy label and possibly different from ya. The noisy data (In, can be viewed as the specific point of the random variables (X, Y) which is from the distribution D. Noise transition matrix T characterizes the relationship between (X, Y) and (X, Y). The shape of T is K K where K is the number of classes in D. The (it element of T represents the probability of flipping clean label Y = i to noisy label Y J. i.e., Tıj := P(Ý = = i). If Y Y always holds, T is an identity matrix. Note the above definition builds on the assumption that T is class-dependent, which is common assumption in centralized learning with noisy labels (Natarajan) al., 2013) Menon et al.) 2015) Liu & Tao 2015). However, in FL., is likely be different for (Wang et al. 202I)). Specifically,
use T to denote the global noise transition matrix for D and T. to denote the local noise transition matrix for D.. In a practical federated learning scenario where the data across different clients are non-IID, different clients may have different label spaces. When the labels are noisy, we naturally have the following definition of openset label noise in FL.
different clients (a.k.a. group-depender
Definition (Openset noisy labels in FI.). The label noise in client c is called openset if. # 5.
Generation of openset noise We propose the following noise generation process to model openset label noise in practical FL systems. Denote by Le,k the indicator random variable that label class is included client c, where 11,4 = 1 (w.p. Qe,k) indicates client c has data belonging to class A and 1e,k 0 otherwise. The indicators {11,,k | Vc [C], [K]} are generated independently with the probability matrix Q, where the (c. k-)th element is Qa,k := E[L.,)] In practice, if all the elements in (De,s(k k [K] are identical, meaning the client observe nothing all the
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classes, then [Ic,t|k [K]] will be re-generated until client is an openset client. Denote by 4 := [c] 1 [C]] the set of clients that include class k. Denote by D(() {nlān k}
the indices of instances that are labeled as class k. For each E [K], instances D(k) will be distributed to clients with I,,4 1 either uniformly non-uniformly as follows.
Randomly sample (without replacement) |/(4)] indices from D and allocate the corresponding instances client Repeat for all E 44--
Uniform allocation:
Non-uniform allocation: Generate probabilities {uelc E I& from Dirichlet distribution Dir(1) with parameter 1 := [1, [,,... 1] (T values). Randomly sample (without replacement) [(()] Me indices from (*) and allocate the corresponding instances to client c. Repeat for all Ig.
In this way, all the clients have openset label noise, i.e., V. + J. Vc [C].
Example Consider the following example. For a data distribution (X,Y) ~ D where Y E y {1,2, ... , the set of all the opensets is the combination of 3 except the full set of Y and the empty set. For example, if y is (1, 1, 2, 3). there would be 2 2 = 6 different combinations of the noisy label space: {1. 2. (1, 2), (1, 3). (2.3) (2.3)} It should be noted that it is still possible that the union of all the clients still cannot cover V. An example of the real and openset T in the 3-class classification problem is as follows. Suppose the real noise transition matrix Treal is shown on the LHS. However, we only observe D. {1, in client c, the optimal estimate of T relying only on D. could only be TopEst even though we know D,. This because when D. = {1,2], we have P(Ý = 3) = = P(Y = 3 3) = O, resulting that the other two probabilities have to be normalized from (1/16, //1,3/110 3/16) to (1/4,3/4) to get a total probability of 1.
0
0
0
0
T
1/3
2/3
0
TopEa
1/3
2/3
0
1/16
3/16
3/4
1/4
3/4
0
Openset noise is challenging A good number of correction approaches in the learning with noisy labels literature would require using the transition matrix T. For instance, loss correction (Patrini et al. 2017) is popular tool to solve the noisy label problem
((((), = { f(x),8)
where T is the transpose of T. The key step of the loss correction approach is to estimate a correct T. However, if the label space is openset, the best estimated T will lead to wrong prediction result. Based on the example above, the best-corrected output is
1
1/3
1/4
[1(x;0)
[i(X;0) + -
T f(x)
0
2/3
3/4
f2(X;0)
0
(x();
where f = [f1, fz, ful The model cannot distinguish class which reasonable. However, it will misclassify class 2 to class 3 because class has larger weight. For example, given instance (r, = 2). the cross entropy loss is 12(2f2(z; 0)/3 + (x; 0)/4) where fa(z; = leads to the minimization of the loss, making the loss correction fail.
3.3 OUR MOTIVATION AND BUILDING IDEA
The above example highlights the challenge of adapting approaches that use noise transition matrix T' to our openset FL setting. Therefore, we hope circle around by building our solutions upon ideas that do not require the knowledge of T. Our design is inspired by the idea of the "contrastive labels". Traditional training relies on positive labels, where the loss is denoted by ((f(x,) ),,,,, Un). However, this kind of loss function is prone to label noise. With powerful model such as deep neural networks, the noisy distribution will be memorized, inducing generalization error (Liu) 2021).
A number of recent works on noisy labels build the idea of the contrastive label either explicitly (eei et al., 2022a Liu & Guo 2020) or implicitly (Patrini et al.] 2017). For example, a "con- trastive label" Wei et al. 2022a) defined by negative label - Yo that introduce negative loss - (x yn.) that punishes the classifier from memorizing the noisy label distribution. Intuitively,
if we know the n-th label is comupted, prefer to make it negative label rather than the traditional
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one since want prevent the model from memorizing the wrong pattern. However, in practice, it is challenging to know whether each individual label is corrupted or not. One tractable solution is to use some "random" negative labels. Notable solutions random peer samples in peer loss (Liu & Guo 2020). We adopt peer loss as our building block, which is free of the knowledge of noise rates.
To be more concrete, in Liu & Guo (020) for each example (0,, Bn), peer loss defines as (an equivalent form):
(f (x)), Mo) :- ((f(an) Bn) - (In),, Dwr)
(4)
where D. is randomly sampled peer label. Later as a follow-up work (Cheng et al. 2020), fcORES was proposed as a more stable version of 4pt. which has the same expectation as EpL:
(f = {(f (xn), Mn) - E [((f())))))
(5)
where Dib is the distribution y given dataset D. Peer loss and ECORES have strong consistency
guarantees. Consider a binary classification problem and e == PPYY = 2 = 1) and e2
P(Y = 1|Y = 2). Then it was proved in Liu & Guo (020) the following robustness of peer loss:
Proposition 1 (Robustness of peer loss (Liu & Guo 2020)). Peer loss is invariant to label noise:
Efl(e((f(X),))
c1 e2) ED (x)),
Moreover, when PCYY =) = 0.5 and is the 0-1 loss, minimizing peer loss on noisy distribution DD is equivalent to minimizing 0-1 loss clean distribution D.
Can we then follow the above idea and implement either EpL or fCORES by requiring each client to sample the "peer label" Ing locally? Unfortunately, the answer is no. Although these methods can skip estimating T, they still need to observe the full data point or full label space to get the correct results local sampling for the peer label Una will lead to a distribution that does not
capture the global one P(Y). again challenging the theoretical guarantees of the existing results. Furthermore, from Liu & Guo (2020), the success of peer loss and other loss correction approaches, would often reply a informative label assumption that 6 E2 < 1. Due to this requirement, now show that for openset noise in FL, In, cannot be sampled from the local Y. directly: if only
class is observed from Y = {1, 2}. e1 = 0 and e₂ = 1 by definition. Therefore, each client will need the label information shared from the other clients. Our idea is rebuild the distribution of noisy label y in the server and send it to the clients so that the clients can be trained with the noisy label Y and 'sss distribution.
PROPOSED METHOD
Recall our design goal is implement either loss (f Fn.) centralized. If this goal is achieved, our solution in the openset FL setting will inherit the established properties in peer loss. As discussed earlier, the first challenge we have is each local client does not have the information to draw the peer sample label Y DyiD-
lOORES (f (xn), ÿn) as if data are
We propose the following label communication-aided algorithm FedPeer, which we also illustrate in Figure [ There are two critical stages to guarantee the success of the proposed methods with good DP protection:
Stage 1: Privacy-preserving global label communication given in Section 4.1
Stage 2: Peer gradient updates the local client using εPL. given in Section and the shared label information from Stage 1.
4.1 LABEL COMMUNICATION
Label privacy protection is an essential feature FL so we cannot pass Y to the other clients, directly. To protect privacy, adopt the label differential privacy (DP) as Definition Definition 2 (Label Differential Privacy (Ghazi et 202T)). Let > 0. randomized algorithm A is said to be e-label differentially private (c-labelDP) if for any two training datasets D and D) that differ in the label of a single example, and for any subset of outputs of A.
P(A(D) E S) S e P(A(D') E).
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Client
A f
D
fi(0)
①
Serve:
Clien
Server
((0)
TDR
22)
Twp
(2
1
Σ
10%
Cliente
8
A /200))
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3
fp. elʃ (ri 2
Semple hrm dieribatioo(ai
DAO y
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Pass Tar
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Pass
Low Calcalation
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Figure 1: The illustration of FedPeer. Step I is the TDP generation where the server generates TD according to and sends each client. After receiving TDP Step the label communication. Every client e calculates DP label Y. according to Top and the noisy label Y.. Clients send Y. to the server. The server aggregates every Y., calculates the posterior label distribution P and sends (T to every client for the peer term sampling. Step 3 the loss calculation using the noisy label Y, on every client c, the model prediction Y, and Y sampled from (TDP)= p and calculate loss. Step is the back-propagation for peer gradient updates.
The high-level idea is to achieve label privacy (DP), each client will use symmetric noise transi- tion matrix Top flip their local labels to protect their labelDP:
if y, if + y.
Tolly, Ø] = PÝ J = v)
where K is the number of classes. Then only the flipped labels are shared between the clients and the server. is easy to show that sharing the flipped labels using Top suffices to preserve labelDP: Theorem (Label Privacy in FedPeer). Label sharing in FedPeer is e-labelDP.
Since we have already known that fooREs is equal to Epr, in expectation so we can calculate the second of Ept. only with the distribution DD. Denote by P the one-hot encoding of 7h The whole label communication process is presented in Algorithm [ At the beginning of the clients. For each
algorithm, the server will initialize Top according to client it calculates the DP label distribution of every data point (If, )4 P% Trp... where P., is the distribution of DP label client c. With this distribution, the client generates the DP private label Vh [N.] for every data point and every client sends all jf back the server. After obtaining all o. from the clients, the server aggregates the label and calculates the posterior label distribution P. To restore the correct distribution of Y, the server calculates (1 Note that (TTP) -T PPE P.)/C P. To apply Tpp and (Tbp) sequentially, FedPeer enables the clients to share the information with the others while DP is guaranteed. Finally, the client calculates the local loss according to Equation where Y sampled from P(Ý = =) == -(TTT) ()) This label communication procedures guarantees c-DP.
broadcast Top to
4.2 FEDPEER
Based on the distribution P()ID), we propose a novel framework based on FedAvg, FedPeer to solve openset noise problem. Denote by (1r) g 0% the variation of model parameters in the r-th round of the local training in client c. Recall 0, is the parameter of fo-
As shown in Proposition I in centralized training, fpt. is proved to be robust label noise. For each local client, if the label of the second term of fpt. is sampled from the distribution of P(L)

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Algorithm Label Communication in FedPeer
Initialization: The server initialize Top according and broadcast DP all clients.
2: for c c C' clients do
>> Client label differential privacy protection
3: calculate p Tpp Vn [Ne]]-
4: generate the private label Vi using P(U i) = P.I.A. VA E [K], E [Nc]-
5: send {ih [neNN] to the server
6: end for
7: The server aggregates the label (1% sent from all clients.
8: The server calculates the posterior label distribution p: p[i] 1/ E E 10,, i).
9: The server calculates (T and sends to each client
The client samples the Y in Eqn. (4) following P(Y = i) ((T -1)
there would be no difference between FedPeer and centralized training in convergence although the client openset noisy. Denote by (r) == g++1 0" the variation of model parameters in the r-th round of the corresponding global gradient descent update assuming the local data are collected to a central server. Define P(D,|D) := IP((X,, Y) ~ D. (X ~ D). detail, it is calculated as Ne Ne/N for client given D. We have the following theorem that guarantees the calibration property of FedPeer.
Theorem (Local clients with FedAvg). The aggregated model update of FedPeer is the same as the corresponding centralized model update, i.e,,
Σ CE|C]
P(D.DD A -
The details of FedPeer are shown in Algorithm 2 At the beginning of FedPeer, the server and the clients e initialize the model and each client e initialize its own dataset De = {Xe, Y2) and loss function f. After this, the server generates the DP matrix Tpp and sends it every client and every client e can generate DP labels Vr, Next, every client sends Vn to the server, and the server aggregates DP labels according Section 4.1 After aggregation the server, posterior label distribution P can be computed and the server sends (TDp) P back to the client so that the client can sample the peer label from this distribution.
Until now, the preparation is finished and we can start the training process. To simulate the practical usage, only part of rather than all clients will participate in the training in one round. The clients are chosen randomly according to the federated fraction X. The selected clients sample the "contrastive label" Y from the distribution (TDP) D and calculate the loss L. according (Liu Guo 2020) by using the output of the model Y.. The model weight updated by The and the server weight averaged according to FedAvg (McMahan al. 2017). which is the end of one communication round.
to
5 EXPERIMENTS AND RESULTS
5.1 EXPERIMENTS SETUP
To validate the generality and effectiveness of FedPeer, we select several public datasets with vari- ous levels of difficulties including CIFAR-10. 10, CIFAR- 100 (Krizhevsky et al. 2009) as benchmark datasets and Clothing- MM (Xiao et al.) 2015). CIFAR-N (Wei et al. (022b) as real-world datasets. To simulate the practical usage, we first apply the noise on the label and generate the openset can- didates according to the number of classes K for every client because only the noisy label is visible to the client the real world. On CIFAR-10 and CIFAR-ION we apply the symmetric noise for benchmark testing while we apply random noise for practical simulation. Furthermore, we also test the performance using Clothing- 1M and CIFAR-N to test the performance of FedPeer in real-world scenarios.
For baseline methods, we use FedAvg (MMMahan et al.) 2017), forward loss correction (LC) Patrini al. 2017), FedProx (Li et al. 2020b), Co-teaching (Han al. 2018) and T-revision Xia al.
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Algorithm 2 FedPeer.
Server: initialize model o. global step size ag and global communication round R.
2: Each Client c: initialize model fc the dataset D. { Fi)) nE | local learning rate de and local updating iterations E.
3: The server generates and broadcasts TDp to all clients according to Definition
4: Clients generate DP labels Vn and send Vn to the server according to Section
5: The server aggregates fy and calculate the posterior label distribution p. to each client.
6: The server send (P)) -
7: for = 1 R do
8: Randomly select C" clients from C according to
9: for in C' clients do
10:
fc ++ff
D Receive the updated weight from the server
for =
12:
In - fe(2n) vn E [No] Sample (we) according 10ppppppp Vn [Ne]-
D. Model Prediction
13:
14:
Calculate loss as La + No Ex=r (C(0)) (% E(Oh, (v()
D Calculate fPL.
15:
fc + fc ae LL
> Model weight updated
16:
end for
17:
end for
18:
f f+= f -0g Ec
D FL. aggregation
19: end for
2019)
methods. The local updating iteration is and the federated fraction À 0.1. The archi- tecture of the network is ResNet-18 (He et al., 2016) for CIFAR dataset and ResNet-50 (He et al.) 2016) with ImageNet (Deng et al., 2009) pre-trained weight for Clothing- IM. The local learning rate is 0.01 and the batch size is 32. The total communication round with the server R is 300 and differential privacy are 3.58, 5.98 and 3.95 for CIFAR-10. 10, CIFAR- 100 and Clothing- 1M, respec- tively. All the experiments are run for times with different random seeds to validate the generality of our methods. Due to the privacy issue in federated learning settings, is hard to find the best model for practical usage. Thus, report both the best in the following tables and the last accuracy in Appendix on the testing set. The details of the implementation of every baseline method in the FL setting be found in the Appendix.
5.2 SYNTHETIC LABEL NOISE
There are two strategies to synthesize the openset label noise in FL.
Symmetric: We first add symmetric label noise (Xia et al. 2019 Han al. 2018) to dataset
D and get D, then distribute D to D.,, Ve following the uniform allocation in Section 3.2 The transition matrix T for the symmetric label noise satisfies Tip n)/(K 1),
D. vi + and Ta
1), Vi [K], where 7) E (0.2, 0.4, 0.6, 0.8) } is the average noise rate.
Random: We first add random label noise (Zhu et al., 2022) to dataset D and get D, then distribute Đ to D... following the non-uniform allocation in Section 3.2 The T random noise is generated follows. The diagonal elements of T for the random label noise is generated by v + Unif( -0.05, 0.05), where v) is the average noise rate, Unif( Unif(-0.05, 0.05) is the uniform distribution bounded by -0.0 and 0.05. The off-diagonal elements in each row of T' follow the Dirichlet distribution (1 - Ta)) -Drr)ir()) where 1 [1, ,1] (K- values). The random strategy is more practical than the symmetric one.
Results and Discussion Ishows FedPeer significantly better than all the baseline methods in the symmetric strategy across almost all the noise rate settings. It is also better than the other methods in most settings of the random strategy. FedPeer very competitive in all the settings. Talso shows directly applying the methods for centralized learning with noisy labels cannot be statistically better than the traditional federated learning solution (FedAvg) and its adapted version (FedProx). indicating the openset label noise in FL is indeed challenging and special treatments necessary to generalize the centralized solution to the FL setting. We also report the accuracy of the last epoch in Table and
5ldd in Appendix.
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Table 1: The performance (the best accuracy) of all methods CIFAR-10 and CIFAR-100
Symmetric
Datanet
Methods
0.2
0.6
0.8
FedAvg
76.89.10.11
63.34.1.822
13.83.0.55
22.1311.25
76.24.1..58
59.1911.01
26.80112.63
21 21.80.10.26
LC
79,1410.35
63.57.0.61
4..331115
22.98.1.60 1.60
74.96.1.92
61.49.3.02
40.52.12118
23.3413.37
43.84-078
25.0W-0.335
65.4-+0.62
54.51-0.73
46.08-122
26:27:00.22
teaching
48.63-0.57
59.0011.19
31.30.12.02
17.1013.78
Trevision
69.16116.20
16.86
31.9312255 56
15.2711.877
66.6915.008
16.22.1.17
31.8112833
17.1210.73
FedPerr
84.77:0.12
75.75:1.99
55.30:1.33
24.6410.55
82.15:10.24
72.69:1.57
1.36
27.55::1.49
FelAvg
47.7610.50
32.63:00.27
20.32::0.51
10.62.10.66
47.75110.2
31.06:10.79
20.14::0.32
9.71::0.43
48.92+0.42
33.15-0..25
10.43+0.45
49.03+00177
32.67-00755
19.78-0067
10.13+0.36
28.1410:25
21.38.10.03
11.6110.44
CIFAR-100 100
Co-teachigg
41.15110.28
29.81:00.72
18:01:10:25
8.73
40.5511.799 79
28.51:1141
18.47::1.95
6.56111.36
T-revision
48.21+0.556
31 35-00.46
17.41=0.22
7.79+00228
48.24+0047
30.91=0.55
16. 16.95=00778
7.46+0..0
FadPeer
53.39-00.43
34.99-166
21.35-0.69
11,02+0.66
51.73+HL36
34,43-0.722
21,35=0.72
10.64+0.43
Table 2: The performance (the best accuracy) of all methods on CIFAR-N and Clothing- M
Datasers
CIFAR-10
CIFAR-100
Clothing-IM
Methods
Wonst
Random
Aggregate
Fine
IM Noisy Training
FedAyg
46.5511.82
59.69.14.88
66.4116.52
1:299
22.59==1.66
70:08
FedProx
58.47=0.97
69.3520.62
74,48=1.00
35.33
65.96
Co-teaching
24.501:2:77
47.341.2105
62.01.11..26
17.83.0.39
40.33
Trreision
57.85_19.44
58.06.18.40
63,4019.99
22.18.11.44
66.95
FedPeer
40.60:
Table 3: The comparison of the influence of different on the performance.
100
3.58
72.4722.64 11.001996
12
73.75=2.38
72.44:552
5.3 REAL-WORK LABEL NOISE
We also test the performance two real-world datasets: CIFAR-N (Wei al. 2022b) and Clothing- 1M (Xiao et al.] 2015). Different from the benchmark datasets, these datasets are corrupted naturally. Clothing- IM is collected from the real website where both data and labels are from the real users. The noisy ratio about 0.4 in Clothing- M.. CIFAR-N consists of CIFAR-10 10 and CIFAR- 100. D, is generated according to the random setting given in Section The labels of CIFAR-N are collected from the human annotation. There are three levels of noisy ratio in CIFAR-I0. 10, worst, aggregate and random while there is only one noisy level in CIFAR-100. It can be found that FedPeer outperforms all the baseline methods the real-world dataset, showing great potential in practical usage.
5.4 EFFECT OF DP LEVEL.
According to Section 4.1 and 4.2 label communication and peer gradient updates local clients two key steps in FedPeer. 6 is the parameter to control the level of DP protection. Following Ghazi] study the influence of the performance. We select the CIFAR-10 corrupted by
ett al. (2021), random noise whose ratio is 0.4 as the method. All the experiments are run with 10 random seeds. In terms of the randomness of model initialization and the noise generation, it be found that FedPeer is stable with the change of e, which agrees with our theoretical guarantee.
6 CONCLUSION
We have defined openset label noise in FL and proposed FedPeer to use globally communicated contrastive labels to prevent local models from memorizing openset noise patterns. We have proved that FedPeer is able to approximate centralized solution with strong theoretical guarantees. Our experiments also verified the advantage FedPeer. Admittedly, FedPeer is only tested with different label noise regimes with synthetic data partitions. Future works include testing FedPeer with real- world FL data partitions and real-world clients such as mobile devices.

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Under review as a conference paper at ICLR 2023
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APPENDIX
Roadmap The appendix is composed as follows. Section presents all the notations and their meaning use in this paper. Section C introduces the implementation details of the experiments and how to apply the centralized training methods to FL. Section D shows the experiment results with more details that are not given in the main paper due to the page limit.
A NOTATION TABLE
Notation
Explanation
!
Noisy ratio
C
Total number clients
Client in federated learning
Y
Random variables
noisy label
Y
Random variables for the output of the model Number of classes in y Transition matrix
K
T
P
The probability
E
The expectation
À
Federated fraction to control the number of clients in every round
0g
The global step size the server side The local learning rate on the client side The loss, the loss function The global communication round The local updating round Differential privacy transition matrix Transition matrix
f
F
The
T
A
The label communication algorithm Labels protected by differential privacy one-hot encoding of yi
p
The posterior label distribution after differential privacy corruption
8, gr
The model parameters, the model parameters of client e at r-th round
A!r
The variation model parameters r-th round the client Random variables for the feature and label
X, Y
N
The space of X. Y
f
The client model, The global model
N. N.
Total number samples, number of samples in client The n-th example the client
(af v
D. = ( vi.) ne
Dataset of client c
D = [( Vn IEEN
Dataset
Is (cl Lek 1, [C}
The vector indicating whether client access class not
Table 4: Table notations used in the paper
B PROOFS
In this section, we present all the proofs of the theorems.
B. PROOF OF THEOREM
Proof. Denote by the label communication algorithm, where the input y and the output Mee- Then after flipping the label y according to the noise transition matrix T, we have
if Vop y.
P(A(y) Yoo
if Yop # y-
-
Accordingly, for another label y/, we have
if yop = y' if you # v.
P(A(y/) = Mor)
g+
K
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Then the quotient of two probabilities can be upper bounded by
P(A(y) = Bup
S E"
P(A(y') \/DP
With Definition we know the above equation is exactly the definition of e-labelDP, i.e. the label sharing algorithm is e-labelDP.
B.2
PROOF OF THEEREM 2
Proof. The centralized peer loss on D is
ED Er](perr( = ED [e((x)))) B ED [e(s()), pn]]
For each client i, the local FedPeer loss is
EDD [(Feddeer(( Y)] = RD, ((((x), V) 3 ED ((((()) 8')]
Denote by (D) the probability of drawing a data point from client i. We have
Σ
PP(D =
KE[C]
Then
Σ
PPD: D))EE, [eadeer (f()),
cE[C]
Σ
PPD, D [(f((x)))) B BDE (()
SEIC]
Ep [(((())))) -. EDy, (0
Each round may include multiple epochs. Suppose there are local epochs. The variation of model parameters in the r-th round of the local training in client can be decomposed by
A == ( o
-((++1,0) 0f 1 0f++1,111)
ofr:1)
DED, ([x),
+... SEp, Fooroor (f(X), Y;0
09
10-01-11.-1)
00
Therefore,
Σ P(D.I)) 0 2Eeeey (DD,, (ererre (f(X), Y; 1
00
0 3 Eiejo I(D; D)ED, [R (f(X), ( ((
00
FED Ppee (f(X), v, 8
00
OEp pwr (fX),, Y: 0(++111))))
80
=A
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C IMPLEMENTATION DETAILS
Platform and Programming Environment We train our model on NVIDIA RTX A500 server with torch and torchvision 1.10 and 0.11, respectively. The details of the baseline methods are as follows.
Loss correction We apply FedAvg in the first 150 rounds to make the weight stable. At the 150th round, the transition matrix of every client will be estimated according to the confidential score of 95%. The predicted label whose confidential score over 95% considered as the ground truth so that we can get every transition matrix of every client. We apply loss correction in the rest 150 rounds according to Equation? 2
networks distinguish the noisy data and the clean data. Similarly, we initialize two same networks when the client initializes and update the two clients in the same way as the original co-teaching network. The also keeps two models. In every communication round, the weights of the two models will average correspondingly.
Co-teaching Co-teaching
T-revision T-revision consists of three steps: estimation of loss correction, and T-revision. In the first 20 communication rounds, the selected clients update the weight at every communication round and all the clients estimate Te. After the 20th round, the selected clients at every commu- nication apply forward loss correction for another 140 rounds. After the 160th round, apply T-revision.
DivideMix DivideMix uses two same networks to distinguish the noisy label. One network is used to assign the pseudo label, the other network is used to the classification. The pseudo label is generated by Gaussian mixture process. In addition, DivideMix uses mix-up data augmentation to boost performance. In FL paradigm, every client will maintain two clients and do the same operation as the centralized training DivideMix.
D EXPERIMENT RESULTS
addition to results best accuracy the methods, we also present the accuracy of the last epoch. The results of symmetric noise are given in Table 5 The results of random noise are given Table The results real-world cases are given in Table 7
Dataset
Methods
0.2
0.4
0.6
0.8
FedAvg
66.90-16226
51.87=6.26
32.73
15.79=1.51
I.C
67.49+3..25
49.17-9.87
32.32++4.57
15.28+2.09
FedProx
66.57+00.5
53.50=0.23
38.01+0.66
20.28=0.90
CIFAR-10
Co-teaching
7..17
69.03-2.25
46.66.1.775
17.88-2.10
T-revision
63.62.3.755
49.76-3.999
29.8313.85
15.34=1.02
FedPeer
78.59
58.35-6.61
46.22+1.31
20.87=2.37
FedAvg
46.79-1.799
30.59
17.15
7.53+0022
LC
47.66:10.19
30.95-0.30
17.2740.19
7.03
FedProx
37.3240.82
28.27-0.40
19.80
11.41-0.31
CIFAR-100
Co-teaching
40.9040.11
29.41=0.64
17.45+0.46
8.2740.96
T-revision
48.02+0.05
31.22-0.40
17.48
7.65+
FedPeer 52.58+0084
33.17=1.16
18.10+0.32
7.78+00.04
Table 5: The performance (the last accuracy) of methods on CIFAR- 10 and CIFAR-100. The noise is symmetric. We compare different methods under different noisy ratios.
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Dataset
Methods
0.2
0.4
0.6
0.8
FedAvg
59.53-13.39
43.14=5.51
30.16:13.73
16.73:2.11
LC
60.63:16.31
43.09=5.77
30.11:2.228
15.1
FedProx
62.86=t4.00
47.62=5.42
36.06:t4.14
19.16=0.64
CIFAR-10
Co-teaching
64.81
54.41=0.80
26.02:12.66
13.43=2.68
T-revision
63.66:15.88
39.70+16.35
34.17:4.24
16.78=0.81
FedPeer
68.18
63.74-2.25
40.41++1997
16.09+1.71
FedAvg
46.3611.18
30.01=1.011
16.7810.337
7.21
LC
48.3
30.69=1.19
17.11=0.40
7.80=0.04
FedProx
37.25=0.52
27.70=0.05
19.92=10445
10.49=0.08
CIFAR-100
Co-teaching
36.08=6.17
25.57=3.61
18.38+1881
6.49=1.45
T-revision
48.01
30.93-0.66
17.10
7.67
FedPeer
51.08
33.24-1.00
17,61
8.16
Table 6: The performance (the last accuracy) of our methods on CIFAR-10 and CIFAR-100. The noise is random. We compare different methods under different noisy ratios.
Datasets
CIFAR-10
CIFAR-100
Clothing- 1M
Methods
Worst
Random
Aggregate
Fine
Noisy Training
FedAvg
41.85+5.83
57.33316.45
65.00=6.51
22.13:2.54
66.91
LC
41.3745.66
58.2015.58
65.4416.51
21.99=1.54
67.75
FedProx
51.10:7..52
65.1915.73
72.30-2.11
34.7710.01
66,97
Co-teaching
24.11+22.03
14.10
60.28+10.54
16.77=0.25
40.37
T-revision
56.16-t21.37
50.90+8.67
58.04+18.13
22.52+1.32
66.79
FedPeer
51.95+7.33
64.50+122.0
8..90
39.78
69.70
Table The performance (the last accuracy) of all methods on CIFAR-N and Clothing-
17


Reviews:
[{"review_id": "_1JYpJik5u", "reviewer": "Reviewer_21aY", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "", "main_review": "", "summary_of_the_paper": "This paper studies federated learning where the local data on each client are non-iid in the sense that they may encounter openset noisy labels. To solve the problem, they extend the so-called peer loss which is studied in centralized label noise learning to the federated learning scenario and call the proposed method FedPeer. They also test the FedPeer method on both benchmark and real-world datasets.", "summary_of_the_review": "The paper is generally interesting to me, but the novelty part is a concern and needs to be discussed carefully, e.g., what are the difficulties and challenges in the federated setting? Analysis of the convergence of the algorithm and more real-world examples are also needed.", "strength_and_weaknesses": "Strength\n\nThe paper considers federated learning with openset noisy labels, which may be practically useful. The proposed method is tested on both benchmark and real-world datasets and is shown to outperform baseline methods.\n\nWeaknesses\n\nThe first concern is about novelty. As peer loss is well studied in centralized label noise learning by Liu & Guo 2020 and Cheng et al., 2020, the main contribution of the current paper is to extend it to the federated learning setting which seems quite straightforward.\n\nAnother concern is about the convergence of the proposed method. As the proposed method contains multiple steps, e.g., initialization with $T_{DP}$ generation, label communication, loss evaluation on noisy data, and peer gradient updates, the rate of openset noise and the selection of $\\epsilon$ may inevitably affect training, how can we guarantee the convergence of proposed method?\n\nThe standard setup of federated learning is to train a model collectively from multiple parties without sharing each party's local data. The label communication step in FedPeer seems necessary to FedPeer but violates the standard federated setting. Could the authors provide some real-world examples of federated learning with openset noisy labels and justify label communication with the label privacy guarantee is enough?", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Nui1p18dkS", "reviewer": "Reviewer_TkFx", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "", "main_review": "", "summary_of_the_paper": "This manuscript studies an interesting and non-trivial problem, which is how to learn with open-set noisy labels under federated settings. The authors first give a very clear and easy-to-understand formulation of the federated open-set label noise problem, which is very useful. They then proposed a federated label-noise learning method by leveraging contrastive labels, and a Differential Privacy (DP)-preserving scheme to collect noisy labels without violating federated learning settings.", "summary_of_the_review": "Overall, this manuscript studies an important and well-defined research problem and proposed a theoretically sound solution. However, the experiments should be more comprehensive to make their contribution more convincing, and whether their DP labels are privacy-preserving given DP transition matrix is debatable. I tend to reject this manuscript under its current version, but I am also willing to adjust my score upon a satisfactory and convincing rebuttal regarding my questions.", "strength_and_weaknesses": "Pros:\n1. The idea of using a transition matrix with a DP guarantee to generate DP label is interesting, I'm not sure if it is new (I'm assuming it is).\n2. The research problem is clearly defined and well-supported by a concrete example, and the solution is strongly motivated.\n\nCons:\n1. The authors should discuss how and why the DP label can still form a complete noisy label space. Based on my understanding, there is no guarantee that the DP label after flipping can still in fact form a complete noisy label space.\n2. If the DP label transition matrix is generated by the server, wouldn't the server be able to invert the uploaded DP label with their respective T?\n3. Authors' example on page 4 seems not correct, if a local T is estimated based on a label space without class 3, the third row of T should be all zeros. Specifically, based on commonly used anchor-point noisy posterior estimation [1,2], since there is no anchor point for class 3, the third row of estimated T should not exist (or all zeros).\n4. The compared baseline methods should include some more recent methods and some state-of-the-art methods that also don't leverage the Transition Matrix for a fair comparison, such as Divide-Mix [3].\n\n[1] Liu, T., & Tao, D. (2015). Classification with noisy labels by importance reweighting. IEEE Transactions on pattern analysis and machine intelligence, 38(3), 447-461.\n\n[2] Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., ... & Sugiyama, M. (2020). Part-dependent label noise: Towards instance-dependent label noise. Advances in Neural Information Processing Systems, 33, 7597-7610.\n\n[3] Li, J., Socher, R., & Hoi, S. C. (2020). Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "6xnAsumhnI", "reviewer": "Reviewer_GxgA", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "", "main_review": "", "summary_of_the_paper": "This paper presents a federated learning method under openset noisy labels. The idea of this paper is from the study of peer loss in centralized training with noisy labels. With global DP matrix, peer loss function can be computed and learn ML models with noisy models. To employ the peer loss in federated learning, the proposed method generates and broadcasts $T_{DP}$ to each clients to compute the estimated global peer loss. From the experiments and ablation studies, the proposed method performs better on datasets with synthetic label noises as well as real-world label noises.", "summary_of_the_review": "This paper presents a new method for federated learning under noisy labels. The proposed method sounds interesting, however, there are several potential issues as discussed in the weakness section. As a result, I vote for \"marginally below the acceptance threshold\".", "strength_and_weaknesses": "Strength:\n+ The idea in this work sounds interesting, which shares a global transmitted matrix in each clients to obtain the posterior label distribution for peer loss computation.\n\n+ From the experiments, the proposed method indeed achieves some good results under label noises, compared with many other pipelines, such as FedAvg, FedProx etc.\n\nWeakness:\n- The initialization of transmitted matrix is important. For a real world federated learning system, how to initialize the transmitted matrix, since the data distributions for individual clients are unknown?\n\n- In experiments setup, it is mentioned “The total communication round with the server R is 300 and differential privacy ε are 3.98, 5.98 and 3.95 for CIFAR-10, CIFAR-100 and Clothing-1M, respectively.”. How do those ε be selected?\n\n- From Table 3, the performance is increased with increasing epsilon values. However, the best performance is obtained when 3.98 is set. 3.98 is very close to 4, which provides much worse performance. The results look unreasonable. Do you have any intuition or explanation for the results? Does the proposed method is not stable enough. If so, how do you select an epsilon value?\n\n- In line 14 of Alg2, which loss do you use to measure the difference between predicted label and GT label? Cross entropy loss, or other loss? Does this loss function affect the results ?\n\n- In Alg2, line 2,4,5 use y, while line 12-14 use Y. The same notation should be used.\n\n- In the definition of T_{DP}, what is the meaning of K ? Parameter K is not explained.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "1WAd90zqlJ", "reviewer": "Reviewer_GPjn", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "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": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "", "main_review": "", "summary_of_the_paper": "The paper defines a openset noise problem in federated learning, and proposes a corresponding solution FedPeer. FedPeer has two critical stages: 1) The author adopts label differential privacy to share the label space of the client, thereby realizing privacy-preserving global label communication. 2) The authors sample the contrastive labels from the global label distribution obtained in the previous stage, and use the output of the local model to compute the peer loss, enabling robust local gradient updates. The proposed method is validated through quantitative comparison with baselines, on multiple datasets (CIFAR-10, CIFAR-100, Clothing-1M, CIFAR-N).", "summary_of_the_review": "Although I think the openset noise problem in federated learning as defined by the authors is interesting and worth discussing in the research community. However, in the method design of FedPeer, the authors designed two stages “Privacy-preserving global label communication” and “Peer gradient updates”, in which both stages directly adopted existing methods and lacked novelty. Therefore, at this stage, I will vote for marginally below the acceptance threshold.", "strength_and_weaknesses": "Strength:\n1. Paper is straightforward and easy to follow, problem is compelling and ideas communicated clearly.\n\n2. Experimental analysis and comparison presented show that the method is useful.\n\nWeaknesses:\n\n1. The openset noise problem defined in this paper is that the observed label space at these clients may be differ as well, even though their underlying clean labels are drawn from the same label space. How is this problem different from the label noise problem in non-iid federated learning scenarios with skewed label distributions?\n\n2. The previous description of the problem is relatively clear, and the example in section 3.2 can be placed in the appendix.\n\n3. Whether the server can use the collected DP label distribution to estimate the original label distribution through the noise transition matrix, which brings the risk of privacy leakage to some extent.\n\n4. When using the symmetric strategy to synthesize openset label noise, the labels are evenly labeled as the wrong class. Does this weaken the difference in label spaces between clients, and thus deviate from the problem scenario addressed by this paper.\n\n5. From the experimental results, the effectiveness of the method is significant when the noise rate is small, but the method seems to fail when the noise rate is high. How to explain this phenomenon?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}]

Author rebuttal:

Decision: Reject