FedOpenMatch: Towards Semi-Supervised Federated Learning in Open-Set Environments
Authors: Hongquan Liu
ChenyuGuo
Yixin Ren
Jihong Guan
Shuigeng Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: semi-supervised federated learning
open-set
federated learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: We propose the first approach, FedOpenMatch, for semi-supervised federated learning with open-set unlabeled client data, which achieves sota performance under both close-set and open-set tests.

Abstract: Semi-supervised federated learning (SSFL) has emerged as an effective approach to leverage unlabeled data distributed across multiple data owners for improving model generalization. Existing SSFL methods typically assume that labeled and unlabeled data share the same label space. However, in realistic federated scenarios, unlabeled data often contain categories absent from the labeled set, i.e., outliers, which can severely degrade the performance of SSFL algorithms.
In this paper, we address this under-explored issue, formally propose the open-set semi-supervised federated learning (OSSFL) problem,  and develop the first OSSFL framework, FedOpenMatch. Our method adopts a one-vs-all (OVA) classifier as the outlier detector, equipped with logit adjustment to mitigate inlier-outlier imbalance and a gradient stop mechanism to reduce feature interference between the OVA and inlier classifiers. In addition, we introduce the logit consistency regularization loss, yielding more robust performance.
Extensive experiments on standard benchmarks across diverse data settings demonstrate the effectiveness of FedOpenMatch, which significantly outperforms the baselines.

Published as a conference paper at ICLR 2026
FEDOPENMATCH: TOWARDS SEMI-SUPERVISED FED- ERATED LEARNING IN OPEN-SET ENVIRONMENTS
Hongquan Liu', Chenyu Guo', Yixin Ren', Jihongg Guan Shuigeng Zhou' Shanghai Key Lab of Intelligent Information Processing, and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China "School of Computer Science and Technology, Tongji University, Shanghai, China {hqliu21, yuxee224yxxxe2211 jhquan@tong edu. en, sqzhou fudan, edu.cn
yxren21 2 @@m. fudan. edu. on
ABSTRACT
Semi- remi-supervised federated learning (SSFL) has emerged as an effective approach leverage unlabeled data distributed across multiple data owners for improving model generalization. Existing SSFL methods typically assume that labeled and unlabeled data share the same label space. However, in realistic federated sce- narios, unlabeled data often contain categories absent from the labeled set, i.e., outliers, which can severely degrade the performance of SSFL algorithms. In this paper, we address this under-explored issue, formally propose the open-set semi- supervised federated learning (OSSFL) problem, and develop the first OSSFL framework, FedOpenMatch. Our method adopts a one-vs-all (OVA) classifier the outlier detector, equipped with logit adjustment to mitigate inlier-outlier im- balance and gradient stop mechanism to reduce feature interference between the OVA and inlier classifiers. In addition, we introduce the logit consistency regularization loss, yielding more robust performance. Extensive experiments standard benchmarks across diverse data settings demonstrate the effectiveness MedOpenMatch, which significantly outperforms the baselines.
INTRODUCTION
Federated learning (FI.) allows multiple clients to collaboratively train models without sharing pri- vate data (McMahan al. [017 Kairouz al. (0221) The privacy preserving nature has driven its wide applications in various fields, such as medicine (Sheller et al. (020) and autonomous driv- ing (Eid Kishawy et al. 2024). However, typical FL assumes that all distributed training samples are fully labeled, which is impractical in most cases as it requires that all clients have enough expertise knowledge, time and willingness to label their data.
fedeeatee learning (SSFL) has emerged an effective solution to exploit Recently, superiieee federated
the distributed unlabeled data with accessing to a limited number of labeled samples (Jeong et al., 2021]. SSFL. generates pseudo-labels for unlabeled data and then involves them into training. SSFL. works can be approximately categorized into two types, label-at-client (Liu et al. 2024 Bai et al, 2024) and label-at-serve (Diao et al.) 2022 Lee al. 2024). with respect to the location of labeled data. This work focuses on the latter setting, which is considered more practical as it does not assume clients have labeling capabilities. Instead, the server (e.g., autonomous driving company) often has plenty resources, and can afford to acquire a small number of labeled samples at a reasonable cost.
While previous works have made great progress in utilizing the unlabeled data, they all assume that unlabeled data share the same category set with labeled data. However, as the clients collect training samples independently and privately, is inevitable for them contain samples from unseen classes, i.e. outliers, so as the testing data. Standard SSFL algorithms do not have the ability to detect outliers and they will assign wrong pseudo-labels for outliers, which has been proven to hurt the model performance (Oliver et al.) 2018). Moreover, outliers will be wrongly predicted as seen classes inference stage, causing inevitable failures in critical application scenarios. Thus, it is critical to take the existence of outliers into consideration when designing SSFL algorithms.
"Corresoonding author

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Client-
Client-
2 2 3
2
Server
Server
3 Nolzbel
No lobel
2 R 4
4 5
Directly train on all data
3
3
Client-22
Client-2
tabel 2345
No dea
2 M
3 4 S
2
6
Lahal 2.M45
lobe
? S Client-3
3
3
g
Label 2365
(a) Standard SSFL
(b) Open-set SSL
(c) Open-set SSFL
Figure 1: Illustration of the differences among (a) standard SSFL where all data are in the same category space ('2", '3', '4', '5"), (b) open-set SSL (OSSL) where the unlabeled data contain samples of unseen classes ('6', '7', '8"), and (c) our open-set SSFL (OSSFL) where unlabeled data in the clients have samples of classes ("6', '7', '8') different from that of the labeled data in the server.
On the other hand, there a related research field called open-set semi-supervised learning (OSSI.) (Saito et al. (02T), which is for centralized scenarios, where the model can access both la- beled and unlabeled data simultaneouslly, and the unlabeled data contain samples of unseen classes. While federated settings pose additional challenges: the strict separation of labeled and unlabeled data makes client difficult to obtain correct supervision, and client training can be easily misled by noisy pseudo-labels. Moreover, data heterogeneity across clients further exacerbates this issue. Therefore, naively adapting those OSSL algorithms into the federated learning setting will suffer severe performance degradation, detailed in Sec. 4.2
To address the problem caused by outliers in SSFL., this paper we first formulate a new problem called Open-set Semi-supervised Federated Learning (OSSFL short), other words, OSSL in FL. Fig. iillstrates the differences among SSFL, OSSL and our OSSFL. Concretely, SSFL is SSL in FL setting; OSSL is SSL in centralized open-set scenarios; and our OSSFL SSL in federated open-set scenarios. To the best of our knowledge, up to now there has been no OSSFL work in the literature. Then, we design the first OSSFL framework, FedOpenMatch. Specifically, we employ an one-vs- all (OVA) classifier serving the outlier detector to help generate high-quality inlier pseudo-labed labels during training. We also introduce gradient stop to address the interference issue between inlier and OVA classifier, and employ logit adjustment for combating the imbalanced OVA prediction, avoid- ing the inliers being recognized as outliers and discarded during training. To further cxploit the unlabeled samples including both unconfident inliers and outliers, devise logit consistency reg- ularization loss by modifying the early proposed probability consistency regularization loss (Saito) al., 2021), which significantly improves the performance. Comprehensive experiments three commonly used benchmarks under various data settings show that FedOpenMatch can significantly boost the open-set accuracy up 14.33% the CIFAR-100 dataset.
In summary, the contributions of this paper are as follows: 1) We formally propose the new prob- lem: open-set semi-supervised federated learning (OSSFL), tackle the outlier issue in SSFL, i.c., to extend OSSL to FL scenarios. 2) We develop simple but powerful OSSFL framework, FedOpen- Match, which consists of three components: gradient stop, logit adjustment, and logit consistency regularization. 3) For comprehensive performance evaluation and comparison, we implement sev- eral OSSFL baselines by adapting the latest OSSL algorithms to the OSSFL setting. 4) We conduct extensive experiments to evaluate FedOpenMatch, which show that FedOpenMatch significantly outperforms the baselines on commonly used benchmarks across diverse data settings.
2 RELATED WORK
Semi-supervised Learning (SSL). SSL is a learning paradigm that leverages both labeled and unlabeled data. Early advances in SSL were largely driven by two techniques: consistency regular- ization (Xie et al. 2020 Miyato et al. 2018) and pscudo-labeling (Lee al. 2013). More recently, FixMatch (Sohn et al., 2020) integrates both strategies into a simple yet highly effective framework, achieving high performance across variety of benchmarks. Building this foundation, subse- quent works have introduced further refinements. For example. FlexMatch (Zhang et al.) 2021), FreeMatch (Wang et al. 2022), Instan'T' (Li et al. 2023a), and Park al. (2025) adopt fine-grained
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Table 1: A qualitative comparison among SSFI, OSSI,, FOSR, and OSSFL from four perspectives.
Distributed
Scarce labels
Open-set unlabeled training Open-set testing
FOSR
x
SSFL.
/
X
OSSL
X
OSSFL
V
thresholding mechanisms to generate high-quality pseudo-labels. SoftMatch (Chen et al. 2023) pro- poses sample re-weighting strategy, while DST (Chen al.] (022) and FlatMatch (Huang al., 2023) enhance robustness against noisy pseudo-labels. Despite their success, all of these methods share a fundamental limitation: they assume that labeled and unlabeled data are drawn from the same category set, which rarely holds in real-world scenarios.
Open-set SSL. This line of research extends conventional SSL scenarios where unlabeled data may contain samples from unseen classes. Early OSSL methods typically follow a detect-and-filte paradigm, aiming to reduce the negative impact of outliers by detecting and discarding them. Dif- ferent approaches have been explored for outlier detection, including ensemble confidence (Chen jct al. 2020), energy-based scoring function (He et al.) 2022), binary classification (Yu et al.) 2020), weighting function (Guo et al. 2020). and cross-modal matching (Huang et al. 2021). More recent methods achieve higher performance by employing one-vs-all (OVA) classifiers as outlier detec- tors (Saito et al. 202 Li et al., 2023b Fan et al. 2023 Hang & Zhang 2024), along with auxiliary strategies such as probability consistency regularizatio (Sato et al. 2021) and negative pseudo- a reliable OVA classifier, IOMatch (Li et al. 2023b) reformulates OSSL into a (K + 1)-way classification problem by grouping all unseen categories into single "unknown" class. In contrast, BDMatch (Hang & Zhang 2024) redefines OSSL as multiple binary SSL tasks, thereby unifying the pseudo-labeling space for both seen and unseen samples.
label mining (Fan al. 2023; Hang & Zhang 2024). To reduce the dependence
Semi-supervised Federated Learning. Existing SSFL works can be broadly categorized into two paradigms: label-at-client, where each client has access to both labeled and unlabeled sam-
ples [Liang et al. 2022, /Zhang et al.] 2023a b. Bai et al. 2024 Liu et al. 2024 Zhang et al. 2024
Liao et al. 2025); and label-at-server, where clients hold only unlabeled data while the server main-
tains small labeled dataset (Jeong et al. 2021 Diao et al. 2022; Kim et al. 2023 Yang et al.)
Lee et al. 2024). This work focuses on the label-at-server setting, which is considered more realistic in practice. A key challenge in SSFL is the generation of reliable pseudo-labels to maximize the utility distributed unlabeled data. Several strategies have been proposed. FedMatch (Jeong let al. (2021) adopts disjoint learning to reduce interference between labeled and unlabeled knowl- edge. SemiFL. (Diao et al., (202)) mitigates pseudo-label degradation by generating pseudo-labels with global model instead of local ones. FT2 (Lee al., 2024) introduces sharpness-aware con- sistency regularization and adaptive thresholding, enabling more effective exploitation of unlabeled data. Kim al. (2023) and Yang et al. (2024) focus on different tasks.
2024
Federated Open-set Recognition (FOSR). FOSR (Yang et al. 2023] Zhu et al. 2023) has the same goal to accurately classify samples from seen classes while detecting outliers from unseen classes, but differs in the training process. FOSR trains models with completely labeled samples from seen classes while OSSFL trains with unlabeled open-set dataset. This distinction makes OSSFL more challenging, as it must tackle the difficulties of semi-supervised learning and open-set recognition.
Summary. To discriminate this paper with existing works, Tab. D presents qualitative comparison between OSSFL and three related research areas SSFL, OSSL and FOSR from four perspectives. Existing SSFL methods are vulnerable to outliers during training and fail to identify outliers during testing. Although Zhang et al. (2023b) introduces open- world SSFL framework, it is limited to the label-at-client setting and pursues different objective than open-set SSFL. OSSL methods share the same goal as OSSFL, but they cannot be directly adopted in the federated setting due the strict separation of labeled and unlabeled data and the data heterogeneity across clients. These limitations motivate propose the OSSFL problem and develop the PedOpenMatch framework.
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Distribution
Client-1
Grad eett Stop
L
Rd
2. Local training
Ag
OVA Classifier
Feature
3. Model Uploading.
Extractor
Client-m
4. Aggregation and Fine-tuning
h2
AT
Classifier
2. Local training
1. Modeel
(a) Model Structure
(b) Training Workflow
Figure 2: (a) The model architecture, consisting of shared feature extractor, an inlier classifier, and an OVA classifier. (b) The training workflow. In each communication round, the server and clients alternatively update their models, which is abstracted into four stages: (I) model distribution, (2) client training, (3) model uploading, and (4) server aggregation and fine-tuning.
THE FEDOPENMATCH METHOD
3.1 PROBLEM DEFINITION AND FRAMEWORK
Problem definition. We assume there are a central server holds a small labeled dataset D3 = (Nazi MenJ N. where Vau Y [l...., K] is the label of sample Za,i and Y is the set of indexes of seen classes, and M clients, each of which has only an unlabeled dataset D {=ma)) N.. Typically, - Nm >>>>>> N.. In the open-set setting, the category of unlabeled sample znzi is not guaranteed be one of the seen classes. The goal of OSSFL. is learn model that can classify the unlabeled samples of seen classes (i.e., inliers) into correct seen classes while identifying the unlabeled samples of unseen classes (i.e., outliers).
Framework. Mathh is multi-task learning framework that jointly trains an inlier classi- fier and an outlier detector. The whole model f consists of shared feature extractor g, an inlier classifier he and OVA classifier ha. For the K-cllasiffcattoon task, the inlier classifier outputs a K-ddimensional logit p {p"] = he o g(x). The OVA classifier is composed of binary classifiers, each distinguishing the k-th target class from the remaining
]k=1
and unseen classes. For a sample x, it outputs 2K dimenssiion logit : {a* {g/ ot) k = ha o g(ø), where o² and of represent the score of being an outlier or an inlier with respect to class k. For simplicity, we denote by P. and p; the inlier predictions of a(x,)) and A(x;) respectively. Here, ar-)) and A(-) denote weak data augmentation (e.g., random cropping and flipping) and strong data augmentation (RandAugment (Cubuk al. 20200). Similarly, d and d denote the OVA predictions.
In each communication round t, the server first trains the model by minimizing
1 N. N.
C.
Σ lee (pe. Ui) Lova lovalgi Mi),
where fee denotes the cross-entropy loss, and lova is the loss of the OVA classifier where we adopt the hega-vegative sub-classifier sampling technique (Saito et 2021, which formulated
Lova (gr, 34) log(g%)) min log(g key
(2)
Once the server training is completed, the updated model is distributed to subset of randomly- selected clients. Then, each selected client m updates the model by optimizing
Lm Cin + Cova Cler
where L/e is the weak-strong logit consistency regularization defined in Eq. 8 Lin is the loss of the inlier classifier with confident inlier pseudo-labels, and I. Lea is the loss of the OVA classifier.
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Specifically, Lm and Lm are formulated follows:
Nm
pin
(I(T()))) Tin) 1 2 0.5)) 0z),
(4)
N
i=1
Nm
N (T Toos) T( Tneg)) (o Maal (,))),
where ÿ² argmax((") is the inlier pseudo-label, r(f:) = max softmaxx))()) computes the confi- dence of inlier prediction, Tin Spos and Tney are predefined thresholds for inlier pseudo-labels, pos- itive and negative OVA pseudo-labels respectively, and lace is the binary cross-entropy loss. After local updates, the client models are aggregated to obtain the new global model f = Em= Wm Sms where {W1,- WpaM } denotes the aggregation weights, and μ presents client join ratio.
To ensure pscudo-label stability during local training, generate pseudo-labels using the global model at the beginning of each round and keep them fixed throughout the client updates. This prevents local training from being biased by noisy labels arising from the scarcity of supervision. Furthermore, we introduce a gradient stop strategy (Sec. 3.2) and logit adjustment mechanism (Sec. 3.3) to further improve the performance.
3.2
STOP GRADIENT FOR DECOUPLED FEATURE SPACE
In our framework, the OVA classifier and the inlier classifier share the same feature extractor, but differ in optimization objectives. The inlier classifier pulls features toward their corresponding class centers while pushing them away from that of the other classes. In contrast, the OVA classifier seeks separate each target class from the remaining classes. Consequently, the two branches inevitably interfere with each other in the shared feature space. Prior OSSL methods (Fan et al.) 2023 Li et al.) 20236) have attempted mitigate this issue by introducing projection layers map features into task-specific spaces. However, the divergence in update directions persists, as evidenced by the low similarity between feature gradients of the two branches (see the results Fig. 5) Appendix(A.2)
To address this challenge, we propose gradient stop (GS), blocking the gradient flow from the OVA branch to the shared feature extractor. The intuition is that the OVA classifier can effectively dis- tinguish inliers from outliers using feature space where intra-class compactness and inter-class separability are already enforced by the inlier classifier. Our ablation studies (Sec. 4.3) further vali- date the effectiveness of this gradient stop strategy.
3.3 LOGIT ADJUSTMENT FOR BALANCED OVA PREDICTION
During training, we observe that most unlabeled samples (including true inlicrs) are classified as outliers and thus discarded, leading to low data utilization rate (see Fig. 10 in Appendix A.2). This phenomenon arises because the OVA classifier is inherently biased toward the majority class-outliers. Specifically, for each class k. all samples of the remaining K-1 classes serve as outliers during training. As K grows, the imbalance between inliers and outliers becomes more severe, which biases the binary classifiers toward predicting outliers, causing many true inliers be mistakenly rejected.
To address this, we adopt a logit adjustment strategy inspired by Menon et al. (021). Specifically, the logits for the OVA loss Lova defined in Eq. (2) are corrected as follows:
+ w log ा.
(6)
where K K represents the class prior probabilities of outliers and inliers, under the assumption of a balanced labeled dataset, and w is a tunable scaling factor. This adjustment counter- acts the imbalance by amplifying the contribution of inlier predictions, preventing them from being overwhelmed by the dominant outlier updates. We further validate the effectiveness of this logit adjustment in Sec. 4.3
3.4 WEAK-STRONG LOGIT CONSISTENCY REGULARIZATION
In sumi-super learning, effectively exploiting unlabeled samples is crucial for improving model performance. In Sec. 3.3 we introduce logit adjustment to ensure that many inliers possible
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utilized. Nevertheless, large portion of unlabeled data including low-confidence inliers and outliers still remain underexploited. Prior OSSL work (Saito et al. 2021 proposed the soft open- consistency regularization loss (SOCR), defined as
- ====NN Nm msse(softmax(())ssfftoo
+ mos///ootnax/p))ssotmmmx//)))/ (7)
where À is a weighting factor. SOCR encourages the model to focus on label-related features by minimizing the divergence between predictions from two augmented views of the same sample. Interestingly, we find that removing the softmax operation and directly enforcing consistency at the logir level yields better performance. We denote this as the logit consistency regularization (LCR):
N
Dler
mse(qi, qi, 4:) mss(ps.
Nm
Ablation studies in Sec. demonstrate that Eq. significantly outperforms Eq. 7 jecture that logit-level regularization provides a stronger and more direct training signal by enfore- ing consistency between raw logits, whereas robabiility-eeee consistency (after softmax) primarily aligns distributions, which does not necessarily ensure consistent decision boundaries.
We
EVALUATION
4.1
EXPERIMENTAL SETUP
Datasets. Following prior studies, we evaluate FedOpenMaach on three widely used benchmarks: CIFAR-10, CIFAR-100 (Krizhevsky et al. 2009), and SVHN (Netzer al. 201T). To simulate data heterogeneity across clients, we sample chint data partitions from a Dirichlet distribution Dir(a), where smaller a corresponds to higher heterogeneitsy. In our experiments, we set a {0.1, 0.3). For each dataset, a subset of classes is designated as seen classes, and a small fraction of them are selected as labeled data. For brevity, we denote the data configuration as X@X@Y Z where D is the dataset name, X is the number of seen classes, Y is the number of labeled samples per class, and Z indicates the partition setting.
Baselines. To validate the effectiveness of FedOpenMatch, compare it against three categories of approaches: (i) labeled data only, which serves as the lower bound; (ii) standard SSFL methods; and (iii) open-set SSFL methods. For standard SSFL methods,
include representative works with publicly available implementations: SemiFL (Diao et al.] 2022). FedLabel (Cho et al., 2023), and FL2 (Lee et al., 2024). We exclude FedMatch since prior work (Diao et al. 2022) consistently reports its performance to be inferior to the labeled-only baseline. To the best of our knowledge, FedOpenMatch is the first attempt to tackle the open-set SSFL problem. As there are no direct base- lines in this setting, we adapt recent OSSL methods originally designed for centralized training into the federated scenario, yielding federated counterparts of OpenMatch (Saito et al.) 2021). SSB (Fan) al. 2023), IOMatch (Li et al. (203b)) and BDMatch (Hang & Zhang 2024). Due space limit, the implementation details are present Appendix A.I
Evaluation Metrics. Following prior work (Li et al. 2023b). we adopt two complementary met- rics to assess performance: (i) Closed-set accuracy evaluates the ability of an OSSL algorithm to leverage open-set unlabeled data in order to improve classification performance only. (ii) Open-set accuracy measures performance in the realistic open-set environment, where both seen and unseen classes coexist. To enable evaluation, treat all unseen classes as a single additional category, yielding a total of K + classes. We report Balanced Accuracy (BA) as:
the classes
K+1
BA K+1
Σ
Recall
k=1
where R denotes the recall for class k.
4.2 MAIN RESULTS
Results on CIFAR-100. Following prior works, we split CIFAR- 100 according to super-classes. Experiments conducted with either 80 or 50 seen classes, treating the remaining classes un-

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Published as a conference paper at ICLR 2026
Table 2: Results of closed-set aceuracy on CIFAR-100 across eight different data settings.
#Seen/Unseen classes
80/20
50/50
#Labels per class
10
25
10
25
Partially
17.29-10.47
18.35+0032
2..02++1.30
22.73-+0.13
SemiFL FedLabel
NeurIPS'22
29.64=0.47
38.81
53.82 43,45=1.20
ICCV'23
35.76=1..99
47.81
47.8410.47
58.71
FT.2
NeurlPS'24 24
35.08
45.23+0001
46.10+0.79
53.95+0.84
OpenMatch
NeurIPS'21
22.25::2.63
34.58
32.93
28,68:116.9
Dir(O.3)
SSB
ICCV'23
5.14=3.97
9.68+11.2
24.28=11.4
44.23:13.21
IOMatch
ICCV'23
37.59+11.29
47.50+1.09
48.35=1.49
16,44
BDMatch
ICML'24
34.05:10.98
42.19=1.33
46.57+2.27
37.45:125.9
FedOpenMatch
Ours
39.45::1004
52.96:10.67
49.9411.72
60.19110.97
SemiFL
NeurlPS'22 22
28.62
35.93=1.15
39.56=22.04
48.00::0.75
FedLabel
ICCV'23
32.8
11.40=1.24
44.13+1.76
50.37+0.73
FL2
NeurIPS'24
32.42
42.87=1.49
42.05+0.52
18.2441.76
OpenMatch
NeurlPS'21
21.83:2.25
33.55
30.92:1.92
1.48
Dir(0.1)
SSB
ICCV'23
8.93=2.93
9.30+13.9
36.1411.65
30.55
IOMatch
ICCV'23
35.13:0.54
43.28
46.4410.88
50.3941.27
BDMatch
ICML'24
32.9542.26
40.4110.53
44.6012.86
35.28
FedOpenMatch
Ours
37.11+0.118
50.39
46.66.10.14
56.54.1082
Table 3: Results open-set accuracy on CIFAR-100 across eight different data settings.
#Seen/Unseen
80/20
50/50
#Labels per class
10
25
10
25
Partially
16.08+00.31
17.71+00.67
21.29-t1.27
17.76:10.44
OpenMatch
NeurlPS'21
2.11::0.77
1.74
1.97=0.01
1.99::0.0
SSB
ICCV'23
4.06:12.81
6.40:17.20
16.95=12.99
28.6243.24
Dir(0.3)
1OMatch
ICCV'23
08.8
37.72++2.06
42.86-0083
19.45++1.2
BDMatch
ICML'24
23.19+11.86
29.38+2.53
39.15++..39
31.144+5.4
FedOpenMatch
Ours
38.97+11.03
50.40+10.42
0..89
59.01
OpenMatch
NeurIPS'21
2.61
2.46
2.1240.27
2.36
SSB
ICCV'23
1.91
6.09
1..08
20.1
Dir(O.1)
IOMatch
ICCV'23
27.20
33.02-10.33
41.10
40.
BDMatch
ICML'24
23.20-11.30
29.51100.99
33.46
30.5
FedOpenMatch
Ours
36.65.12.43
47.35-12.98
43.91
5..60
seen. For each seen class, 10 or 25 samples are selected as the labeled set. Tab. 2 and Tab. preset the results of closed-set accuracy and open-set accuracy, respectively. Since standard SSFL algorithms cannot detect outliers, open-set accuracy is only compared among OSSFL approaches. FedOpenMatch consistently outperforms existing methods on both metrics. For example, under the CIFAR- 100 @0@ 25 Dir(O.1) setting, closed-set and open-set accuracy are improved by up to 7.1 5% and 14.33%, respectively, demonstrating the effectiveness of our approach.
Results on CIFAR-10 and SVHN. For CIFAR-10, we follow prior studies (Li et al. 2023b) and select the animal classes as seen classes. For SVHN, classes 2 through 7 are treated as seen. For each seen class, 25 or 40 samples
randomly selected the labeled set. Results of closed-set and open- set accuracy are presented in Tab, and Tab. respectively. The results highlight the improvements achieved by FedOpenMatch, particularly under highly heterogeneous settings (Dir(0.1)).

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Published as a conference paper at ICLR 2026
Table 4: Results of closed-set aceuracy on CIFARIO and SVHN.
Dataset-#SSen/Unneen classes
CIFAR 10-6/4
SVHN-6/4
#Labels per class
40
25
40
25
Partially
38.35+1.11 4..47+10.3
70.29+4..83
51.55-+3.88
SemiFL FedLabel
NeurIPS'22
33.96=17.55
19.02#2.48
70.75+8.10
52.57=14.00
ICCV'23
70.21#1009
64.0912.08
83.75+0.15
79.63
FT.2
NeurlPS'24
55.69+1.33
53.39+2.79
77.93+1442
65.09++3.4
OpenMatch
N1
15.42+1.553
16.50+0.28
36.54+15.0
16.31+33114
Dir(0.3)
SSB
ICCV'23
16.67+0000
16.67+0.00
12.41+0.00
16.1943.28
IOMatch
ICCV'23
72.32:10.42
7..0510.34
84.3411.17
80.83:10.49
BDMatch
ICML'24
53.16:127.8
19.7
82.1242.01
57.61
FedOpenMatch
Ours
73.32::1.12
1..45+1.26
84.86
10.03
SemiFL
NeurlPS'22 22
18.50=16.7
17.26+00.3
70.03=4.41
47.05=14.80
FedLabel
ICCV'23
59.36:10.59
5..22+1.93
77.67+2.64
69.30#3.37
FI,2
NeurlPS'24 24
50.82++.007
45.29
72.73+2.53
65.43
OpenMatch
NeurIPS'21
16.68=0.16
16.6310.04
63.66112.3
13.71
Dir(0.1)
SSB
ICCV'23
16.67=0.00
16.67=0.00
12.41
14.30
IOMatch
ICCV'23
64.79:1.55
59.60
79.48
16.5
BDMatch
ICML'24
36.87121.7
51.73+3.22
80.92
58.33428.3
FedOpenMatch
Ours
65.1912.83
60.8910.71
82.63
7..36.10.74
Table 5: Results of open-set accuracy on CIFAR10 and SVHN.
Dataset-4SeennUnseen
CIFARI0-6/4
SVHN-6/4
#Labels per class
40
25
40
Partially
36.02+00.66
0..3
60.44+4.59
43.93++..71
OpenMatch
NeurIPS'21
14.28:10.00
14.28+10.00
14.29+0.00
14.28=10.00
SSB
ICCV'23
14.3910.18
14.42-10.24
14.2910.00
14.30-10.03
Din(O.3)
IOMatch
ICCV'23
61.65:10.48
61,8240.42
71.78
68.52-0088
BDMatch
ICML'24
42.62-124.5
24.59
19.3611.48
48.3
FedOpenMatch
Ours
62.26:10.51
61.9812.18
71.8710.83
67.75
OpenMatch
NeurlPS'21
14.28+00.00
14.29+0000
53.59+15.2
14.29-10.00
SSB
ICCV'23
14.30+0.02
14.33+10.07
15.65+2.114
14.48-10.34
Dir(O.1)
IOMatch
ICCV'23
54.94+1.37
50.71+1.78
68.1340.21
64.41
BDMatch
ICML'24
25.86+120.1
24.65+117.9
67.49#1.18
46.47+27.9
FedOpenMatch
Ours
55.27-12.21
51.45:10.45
7..97
66.70+11002
Comparison under complex data settings. To better simulate real-world data complexity, construct a variant of CIFAR-100, denoted as CIFAR 100-C, by applying diverse corruption types following the protocol of (Hendrycks & Dietterich 2019 In this dataset, 70% of the samples are corrupted to induce feature shift, while the remaining 30% are kept clean. Each client then sam- ples its local data according to a Dirichlet distribution (consistent with our main experiments) to additionally introduce label shift. This setup reflects realistic scenarios where both feature shift and label shift cocxist. Under these challenging conditions, FedOpenMatch still achieves the best per- formance by clear margin as shown in Fig. 13 in Appendix A.2 demonstrating strong robustness to complex and heterogeneous data distributions.
Comparison under scenarios with high unknown-class prevalence. To further evaluate FedOpen- Match under scenarios where unseen prevalence is very high, we conduct experiments on CIFAR-
We follow the official instructions http://gttta..ccoommmntttkksstst
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Table 6: Ablation studies isolating each component.
1i
Base
Base+GS
Base + GS + LA
Base + GS + LA LCR
Open-set Accuracy
34.81
39.51
43.65
48,36
Closed-set Accuracy
45.86
46.14
45.81
50.38
100 dataset with only 20 seen classes and the remaining 80 classes are unknown. Figure [4] in Appendix (A. shows that FedOpenMatch significantly outperforms baselines, demonstrating ef- fectiveness when unknown classes dominate the data distribution.
Summary. The results above show that directly adapting OSSL algorithms to the federated set- ting leads to unstable performance. For instance, SSB and OpenMatch often fail to surpass the lower-bound baseline, despite sharing the same backbone architecture our method. To investi- gate the cause, we visualize the utilization rate and pseudo-label accuracy in Figs. [0 and [1 in [A.2] As illustrated, OpenMatch maintains a persistently low utilization rate of unlabeled data throughout training. This is mainly due to its "detect-and-filte strategy, where the imbalanced OVA classifier mistakenly identifies most samples as outliers, leaving a large portion of informative data underutilized. This observation highlights the necessity of logit adjustment, In contrast, SSB achieves higher utilization by incorporating all samples with confident inlier predictions, regardless of whether they inliers outliers. However, its pseudo-label accuracy remains low, which ulti- mately constrains overall performance. Since clients in OSSFL lack reliable supervision, the models highly susceptible incorrect supervision, leading to progressively worse pseudo-labels.
Appendix A.2
FetCpenMatch addresses these challenges by generating pseudo-labels with the global model and keeping them fixed during local training, thereby avoiding degradation of pseudo-label quality caused by unstable updates. Coupled with the proposed components, the data utilization rate steadily increases while maintaining relatively high pseudo-label accuracy. As a result, FedOpenMatch achieves consistent state-of-the-ee performance across nearly all settings, demonstrating both its robustness and superiority, particularly under challenging data configurations.
4.3 ABLATION STUDIES
In this section, we evaluate the effectiveness of key components in FedOpenMatch, and examine its sensitivity to syper-parameters. Due to space limit, we put the detailed results in Appendix AA.
Ablation studies isolating each component. We conduct experiments to demonstrate the effective- ness and necessity of each component, as summarized Tab. 6 The results show the effectiveness and necessity of each module, confirming that each component contributes meaningfully to the over- all performance of MedOpen.atchh
Effect of gradient stop (GS). We conduct experiments under CIFAR100@500 10@ configura- tions to investigate the effect of GS. As shown in Fig. 3 and Fig. g in Appendix [A.2] with the adoption of GS, the performance consistently improved, with the open-set accuracy gap reaching up to 10.68%. Mcanwhile, we observe that the gradient similarity between the inlier classifier and OVA classifier branches is highly unstable. When their optimization directions happen align, the two branches temporarily reinforce each other (e.g., in the early training stage without GS, the ac- curacy can exceed that with GS). However, as the gradient similarity drops, the branches interfere with each other and overall performance degrades. In other settings (e.g., Fig. 5 in Appendix A.2 where the similarity remains low throughout training, the performance without GS is consistently worse than with GS. Overall, GS is important for achieving stable and superior performance.
Effeet of logit adjustment. To mitigate the imbalance between inliers and outliers in OVA training (Sec. 3.3), we introduce logit adjustment. We evaluate its effect on CIFAR- 80 @ 25 @ 10) under different adjustment weights w. As shown in Fig. 7 and in Fig. g in Appendix A.2 open-set accuracy drops significantly (18.19%5 when O, as the OVA classifier becomes biased toward the majority classes (unseen classes). Performance peaks when w = 1, validating the effectiveness of logit adjustment in correcting class imbalance.

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Effect of logit consistency regularization. We propose a weak-strong logit consistency regulariza- tion to better exploit unlabeled samples (Sec. 3.4) We study the effect of the consistency weight on CIFAR- 100, with results in Fig. 7 in Appendix A.2 Both closed-set and open-set accuracy sensitive to X. When À = 0, closed-set and open-set accuracy drop by 4.82% and 5.61% %, respec- tively, indicating the necessity of logit consistency. 'Tee best closed-set accuracy is achieved at À 2 (improving 0.9% over x= 1), but with a 4.7% drop in open-set accuracy. Overall, À = provides the best trade-off between closed-set and open-set performance. We further compare our proposed logit consistency loss (Eq. (8) with the probability consistency loss (Eq. 7 ). As shown in Fig. Z Appendix A.2, our LCR loss significantly boosts both closed-set set and open-set accuracy. for exam- ple, by 4.83% and 3.68% under data configuration CIFARI 1 80 25 @ Dir(O.1), 1), demonstrating its superior ability to transfer richer information.
Influence of local epochs. To understand the influence of client local training epochs, we conduct additional experiments on CIFAR- 100 dataset. As shown ?? Appendix(A.1 using too few local epochs leads to underfitting and slow convergence, requiring many more communication rounds to achieve reasonable performance. In contrast, using too many local epochs can improve accuracy but introduces the risk of unstable or divergent training due to excessive local drift. The conclusion is consistent with prior SSFL work (Diao et al. 2022).
Influence of OVA threshold. To understand the influence of OVA threshold, we conduct exper- iments on CIFAR-100 100 with varying OVA threshold. The results are presented in Fig. 12 in Ap- pendix A.2 which suggests that closed-set accuracy remains stable across wide range of OVA thresholds, indicating that this component has limited impact on inlier classification. In contrast, open-set accuracy is more sensitive to the threshold value. A low threshold admits more samples during training but also introduces substantial noise, leading to unstable optimization and degraded open-set performance. Therefore, using higher threshold generally preferable, as it filters out unreliable samples and stabilizes training.
Comparison with different backbones. To further assess the effectiveness and robustness of Fe- dOpenMatch across model architectures, we conduct additional experiments using NedeResNet- 28x2, a widely adopted backbone in the SSL and SSFL literature. As shown in Tab. 7 in Ap- pendix A.2 FedOpenMatch consistently achieves the best performance, demonstrating that its com- ponents do not rely on any backbone-specifie assumptions.
CONCLUSION
In this work, we formulate the practical yet underexplored problem of open-set semi-supervised federated learning, where the distributed unlabeled dataset contains samples from classes unseen in the labeled data. To address this challenge, we propose the first OSSFL framework, FedOpenMatchh, which effectively leverages distributed open-set unlabeled data to improve model performance under both closed-set and open-set scenarios. We conduct extensive experiments on widely used bench- mark datasets, demonstrating that, despite its simplicity, FedOpenMatch outperforms existing ap- proaches by a clear margin, particularly under complex data configurations. We also provide detailed analyses validate the contribution of each proposed component.
LIMITATION AND FUTURE WORK
While FedOpenMatch demonstrates significant empirical gains, rigorous theoretical understand- ing of its underlying mechanisms remains incomplete an open challenge shared by many SSL approaches. We leave a more formal theoretical analysis future work. In addition, several promis- ing extensions remain. For example, the inlier and OVA classifiers may complement each other when their optimization directions are aligned, suggesting the potential of adaptively controlling the gradient stop mechanism or explicitly promoting consistency between their optimization processes.
ACKNOWLEDGEMENTS
The computations in this research were performed using the CFFF platform of Fudan University. Jihong Guan was supported by National Natural Science Foundation of China (NSFC) under grant No. 62372326.
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REFERENCES
Sikai Bai, Shuaicheng Li, Weiming Zhuang, Jie Zhang, Kunlin Yang. Jun Hou, Shuai Yi, Shuai Zhang, and Junyu Gao. Combating data imbalances in federated semi-supervised learning with dual regulators. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10989- 10997, 2024.
Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, and Mingsheng Long. Debiased self-training for semi-supervised learning. Advances in Neural Information Processing Systems, 01322224-324332 2022.
H Chen, R Tao, Yue Fan, Y Wang, M Savvides, Wang, B Raj, X Xie, and Bernt Schiele. Softmatch: Addressing the quantity-qualin tradeoff in semi-supervised learning. In Eleventh International Conference on Learning Representations, pp. 21. OpenReview. net, 2023.
Yanbei Chen, Xiatian Zhu, Wei Li, and Shaogang Gong. Semi-supervised learning under class distribution mismatch. In Proceedings of the AAAI conference on artificial intelligence, pp. 3569- 3576, 2020.
Yae Jee Cho, Gauri Joshi, and Dimitrios Dimitriadis. Local or global: Selective knowledge assim- ilation for federated learning with limited labels. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 17087-17096, 2023.
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition workshops, pp. 702-703, 2020.
Enmao Diao, Jie Ding, and Vahid Tarokh. Semifl: Semi-supervised federated learning for unlabeled clients with alternate training. Advances in Neural Information Processing Systems, 35:17871- 17884, 2022.
Mohab M Eid Kishawy, Mohamed T Abd El- El-Hafez, Retaj Yousri, and M Saced Darweesh. Federated learning system on autonomous vehicles for lane segmentation. Scientific Reports, 14(1):25029, 2024.
Yue Fan, Anna Kukleva, Dengxin Dai, and Bernt Schiele. Ssb:
Simple but strong bascline for boosting performance of open-set semi-supervised learning. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 16078, 2023.
Lan-Zhe Guo, Zhen-Yo Yu Zhang, Yuan Jiang, Yu-Feng Li, and Zhi-Hua Zhou Safe deep semi- supervised learning for unseen-class unlabeled data. In International conference on machine learning, pp. 3897-3906. PMLR, 2020.
Jun- Yi Hang and Min- Zhang. Binary decomposition: A problem transformation perspective for open-set cuni-super learning. In Forty-first International Conference on Machine Learning, pp. 1-14, 2024.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 770-778, 2016.
Rundong He, Zhongyi Han, Xiankai Lu, and Yilong Yin. Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data. In Proceedings of the IEEE/CVE conference on com- puter vision and pattern recognition, pp. 14585- 14585-14594, 2022.
Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common cor- ruptions and perturbations. arXiv preprint 1903 12261, 2019,
Junkai Huang, Chaowei Fang. Weikai Chen, Zhenhua Chai, Xiaolin Wei, Pengxu Wei, Liang Lin, and Guanbin Li. Trash treasure: Harvesting ood data with cross- modal matching for open-set semi-supervised learning. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 83 3310-8319, 2021.
11

---

Published as a conference paper at ICLR 2026
Zhuo Huang, Li Shen, Jun Yu, Bo Han, and Tongliang Liu. Flatmatch: Bridging labeled data and unlabeled data with cross- -ross-sharppess for semi sami--upervised learning. Advances in neural information processing systems, 36: 36:18474-18494 2023.
Wonyong Jeong, Jachong Yoon, Eunho Yang, and Sung Ju Hwang. Federated semi-supprvised learning with inter-client consistency & disjoint learning. In International Conference on Learning Representations, pp. 1-15, 2021. CRLLttts::/ /tt.ttp::///peeeeeeeaee http:///ppeeeeeeeeeeeeeee hwtp:/ewwweeee.oeeeeeeeeeeeeeeee
ce6CFXBh30h
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Ad- vances and open problems in federated learning, Foundations and Trends@ in Machine Learning, 14(1-2):1-211 2021.
Taehyeon Kim, Eric Lin, Junu Lee, Christian Lau, and Vaikkunth Mugunthan. Navigating data heterogeneity in federated learning: A semi-supervised approach for object detection. In Thirty- seventh Conference on Neural Information Processing Systems, pp. 1-23, 2023.
Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images, 2009.
Dong-Hyun Lee et al. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, volume 3, pp. 896. Atlanta, 2013.
Seungjoo Lec, Thanh-Long V Le, Jaemin Shin, and Sung-Ju Lee. fl:: Overcoming few labels in federated supervise learning. Advances in Neural Information Processing Systems, 37: 43693 016693447714, 2024.
Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, and Tongliang Liu. Instant: Semi- supervised learning with instance-dependent thresholds. Advances in Neural Information Pro- cessing Systems, 36:2922-2933 2023a.
Zekun Li, Lei Qi, Yinghuan Shi, and Yang Gao. lomatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15870-15879, 2023b.
Xiaoxiao Liang, Yiqun Lin, Huazhu Fu, Lei Zhu, and Xiaomeng Li. Rscfed: Random sampling consensus federated semi-supervised learning. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pp. 10154-10163, 2022.
Dongping Liao, Xitong Gao, Yabo Xu, and Cheng Zhong-Zhongaa Xu. Progressive distribution matching for federated semi-supervised learning. In Proceedingy of the AAAI Conference on Artificial Intelligence, pp. 5191-5199, 2025.
Yuzhi Liu, Huisi Wu, and Jing Qin. Feded: Federated semi-supervised learning with class awareness balance via dual teachers. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3837-3845, 2024.
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Ar- cas. Communication Eitiiinnn-ffiittttt Learning of Deep Networks from Decentralized Data. In Aarti Singh and Jemry Zhu (eds.), Proceedings of the 20th International Conference Artifi- cial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pp- 1273-1282. PMLR, 20-22 Apr 2017. URL httph:tt//////.t http::::///ccceeddeeee waw.oocceeiingg.....o www.mmrr.rree//S... momahan17a.. html
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. Long-tail learning via logit adjustment. In International Conference on Learning Representations, 2021. URL https: : oper revi ew. net. forum?? 3ttp::1==33nnnocoooo
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. Virtual adversarial training: regularization method for supervised and supervssee learning. IEEE transactions on pattern analysis and machine intelligence, 1999 2018.
12

---

Published as a conference paper at ICLR 2026
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng. et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep featur
learning and unsupervised feature learning, pp. 4. Granada, 2011.
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, and Ian J Goodfellow. Realistic eval- uation of deep semi-supervised learning algorithms. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 3239-3250, 2018
Jae Hyeon Park, Joo Hyeon Jeon, Jae Yun Lee, Sangyeon Ahn, Min Hee Cha, Min Geol Kim, Hyeok Nam, and Sung In Cho. Dynamic pseudo labeling via gradient cutting for high-low entropy exploration. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp- 20602-20611, 2025.
Kuniaki Saito, Donghyun Kim, and Kate Saenko. Openmatch: Open-set semi-supervised learning with open-set consistency regularization. Advances in Neural Information Processing Systems, 2021.
Micah J Sheller, Brandon Edwards, G Anthony Reina, Jason Martin, Sarthak Pati, Aikaterini Kotrot- sou, Mikhail Milchenko, Weilin Xu, Daniel Marcus, Rivka R Colen, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific reports, 10(1):12598, 2020.
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems, 33:596-608, 608, 2020.
Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, al. Freematch: Self-adaptive thresholding for semi- supervised learning. In The Eleventh International Conference on Learning Representations, pp. 20, 2022.
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. Unsupervised data augmentation for consistency training. Advances in neural information processing systems, 33:6256-6268, 2020.
Chen Yang, Meilu Zhu, Yifan Liu, and Yixuan Yuan. Fedpd: Federated open set recognition with parameter disentanglemenn. In Proceedings of the IEEE/CVF International Conference on Com- puter Vision, pp. 2023.
Mingzhao Yang, Shangchao Su, Bin Li, and Xiangyang Xue. Exploring one-shot semi-supervised federated learning with pre-trained diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 16325-16333, 2024.
Qing Yu, Daiki Ikami, Go Irie, and Kiyoharu Aizawa. Multi-task curriculum framework for open- set semi sumi-supervised learning. In European conference computer vision, pp. 438-454. Springer, 2020.
Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki. Flexmatch: Boosting semi-supervised learning with curriculum pseudo la- beling, Advances in Neural Information Processing Systems, 34: 8408-18811111 18419, 2021.
Chao Zhang, Fangzhao Wu, Jingwei Yi, Derong Xu, Yang Yu, Jindong Wang, Yidong Wang, Tong Xu, Xing Xie, and Enhong Chen. Non-iid always bad? superviie heterogeneous federated learning with local knowledge enhancement. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 3257-3267, 2023a.
Jie Zhang, Xiaosong Ma, Song Guo, and Wenchao Xu. Towards unbiased training in federated open- world semi-supervised learning. In Andreas Krause, Emma Brunskill, Kyunghyun Cho. Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.). Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 41498-41509, PMLR, 23-29 Jul 2023b. URL https::/ v202 zhang2aannaaaa html
UtLatttpp::/ppoeeeiiico http:////cceeeeeig..e/e .mrr. .wl..press/
13

---

Published as a conference paper at ICLR 2026
Yonggang Zhang, Zhiqin Yang. Xinmei Tian, Nannan Wang, Tongliang Liu, and Bo Han. Robust training of federated models with extremely label deficiency. The Twelfth International Con- ference Learning Representations, pp. 1-22, 2024.
Meilu Zhu, Jing Liao, Jun Liu, and Yixuan Yuan. Fedoss: Federated open recognition via inter- client discrepancy and collaboration. IEEE Transactions on Medical Imaging, 43(1):190-202, 2023.
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Table 7: Performance comparison of FedOpenMatch with state-of-the-ard baselines using different backbone (WideResNet-28x2] The results show that FedOpenMaach consistently achieves the best performance with clear margin, demonstrating its robustness different backbone architectures.
CIFAR-100 20@2500 50@25@Dir(.11
50@25@ Dir(O.1) 80@25 @ Dir(0.1) 80@25@ Dir(0.1)
Open-set Acc
Closed-set Acc
Open-set Acc
Closed-set Acc
FedLabel
None
47.12
None
37.05
10Match
35.71
44.58
24.65
37.12
FedOpen.aatch
50.16
52.14
42.69
43.23
A EXPERIMENTAL DETAILS
A.1 IMPLEMENTATION
In our experiments, we employ ResNet-18 (He et al., 2016) as the backbone for all datasets and methods, with the classification head implemented as described in the original papers. The number of clients is set to 50 for CIFAR 100@ 50@- - and 100 for the other settings. In each communication round, 10 clients
randomly selected for local training. Following prior work, assign equal weight to each local model for aggregation.
We train the models for 600 communication rounds and adopt SGD with momentum of 0.9 and weight decay of 0.0005 as the optimizer. The learning rate is initialized at 0.03 and decayed using cosine schedule. The number of local training epochs for clients and server are to E. 5 and E, 5, respectively. For all methods, the client batch size is 32, and the server batch size is 10 or 250 depending on whether the number of labeled samples is fewer greater than 1000. Following prior work, we employ confidence thresholds Tin = 0.95 for inlier pseudo-labels, and Tpos 0.99, Tneg 0.01 for OVA pseudo-labels. The weight for logit adjustment is set LCR (Eq. 8 is set to À across all settings.
and the weight
Fair Comparison. To ensure fair comparison, we implement the federated versions of the baseline OSSL algorithms following their official implementation and use the hyperparameters recommended in their respective papers. The data distribution is consistent across all methods. We conduct three independent runs all datasets using random seeds (0, 10, and 100). After each communication round, we evaluate the global model on the original test set and report the mean and standard error of closed-set accuracy and open-set accuracy.
All experiments are conducted on an Ubuntu 16.04 server using PyTorch 1.8.0. The source code and detailed library dependencies provided in Supplementary Material.
A.2 DETAILED EXPERIMENTAL RESULTS.
Due to the space limitation, we present the detailed experimental results here.
Computation/ommuniattioo overhead. It is worth north-noting that OVA classifier does not introduce much additional communication and computation overhead as it only consists of a two-layer projec- tion head and a linear classification head. The number of computation and communication overhead are negligible relative to the backbone.
B THE USE OF LARGE LANGUAGE MODELS
During the preparation of this manuscript, we used ChatGPT to assist with writing by providing the prompt: "I am preparing a paper for submission to an international conference and would like your help to check for any grammatical issues and refine the wording or sentence structure where necessary to ensure conciseness and precision." We applied its suggestions paragraph by paragraph, and all outputs were edited by to ensure correctness.
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Accursyt.cccuacyy
Closed-sed set Accuracy
Gradient Similarity
Fod0ocnkkatch w/ 65
Rod0ponloach
Fedopenifathh wib 65
FodOpcoMateh
Nor 1D typ Din0.1
Nocurscy (96)
Aucuracy Phu
0.5
0.4
0.2
Step
Step
Step
1.00
200
300
400
500
100
200
300
100
0 100 200
300
400
300
Figure 3: Performance comparison of FedOpenMateh with and without the Gradient Stop (GS) strategy on CIFAR-100@ I0@Dir(() Dir(O.1), along with the gradient similarity between the inlier classifier and OVA classifier branches. In the early training stage, the performance without GS surpasses that with GS, suggesting that the OVA classifier and inlier classifier can reinforce each other when their update directions are aligned. However, as training progresses, the gradients begin diverge, and the resulting interference between the two classifiers leads a performance drop.
Open set Accuracy
Closed-sed Accuracy
Gradient Similarity
FedDpentttee
Red0penllatchwee FedOpenttater
50
Accuratyy
45
0.5
40
0.5
20
10
25
1.0
200
300
400
500
100
200
300
400
500
100 200
300
400
500
Figure 4: Performance comparison of FedOpenMatch with and without the Gradient Stop (GS) strategy on CIFAR-I00@ 50@ @ Dir(O.3), along with the gradient similarity between the inlier classifier and OVA classifier branches. Since the update directions diverge for most of the training process, the performance with GS surpasses that without GS, as expected.
Open-set Accuracy
Closed-set Accuracy
Gradient Similarity
Tec Micige w/o TedipentMoch
Won-lD De(..1
sccuioty HH]
50
40
45
0.6
40
30
20
300
500
300
300
400
Figure 5: Performance comparison of FedOpenMatch with and without the Gradient Stop (GS) strategy on CIFAR-100@ 50@ 25 Dir(O.1), I), along with the gradient similarity between the inlier classifier and OVA classifier branches. The gradient similarity remains low throughout the training, thus the performance with GS consistently surpasses that without GS.
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Closed-set Accuracy
Open-set Accuracy
Gradient Simiarity Has haie
www.eeeeeewweeeeeeeeeooe www.
40
30
30
20
10
-0.5
Step
Step
500
Figure 6: Performance comparison of FedOpenMatch with and without the Gradient Stop (GS) strategy on CIFAR-100@ 50@ 25@ Dir(0.1) with different backbone (WideResNet-28x2)) along with the gradient similarity between the inlier classifier and OVA classifier branches. The gradient similarity remains extremely low during the whole training process, which proves the clear perfor- mance gap between models with and without GS.
70
60
Closed
Clesed
acuracy clasass)
Open-ea senmey
Closalaut
Openn
58.75
Open-sct
Accuncy (%)
Accurey (%)
Accuncy (%)
47.5
25
36
0.5
1.25 1.5
80g:25
806/10
506925
506000
(a) Weight of Logit Adjustment
(b) Weight of LCR
(c) LCR vs. SOCR
Figure 7: Ablation studies. For simplicity, use X@Y to represents data setting with X seen classes and Y labeled samples per class. (a): FedOpenMatch with different w under CIFAR-1000 @25@ @ Dir(O.1) setting. Without logit adjustment (w 0) the open- set accuracy degrades signif- icantly. (b): FedOpenMatch with different under CIFAR- 102-1006 100@ 80@ 2 Dir(0.1) setting. The performance is improved with LCR loss. (c): Comparison of existing SOCR loss and our LCR loss under CIFAR100 1AAA000@-@0 @ Dir(O.1) setting. Our LCR outperforms existing SOCR across various settings.
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Figure& 8: Learning curves of training with various strength of logit adjustment (Eq. O0) on CIFAR- 100 dataset. The results present a clear accuracy margin between those with and without logit adjustment.
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Learning curves of training with various weights of logit consistency regularization loss (Eq. 8 on CIFAR-I00 dataset. The performance gets clear improvement with logit consistency
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Figure 10: Comparison of utilization rate and pseudo-label accuracy of OpenMatch (Saito et al., 2021), SSB (Fan et al. 2023) and MethpenMatchh (ours), which share the same architecture. Open- Match suffers from both a low data utilization rate and poor pseudo-label quality. as most samples are mistakenly identified as outliers by the imbalanced OVA classifier, thereby limiting its perfor- mance. SSB relaxes the pseudo-label filtering strategy to increase data utilization, but this introduces a large number of incorrect pseudo-labels. In contrast, FedOpenMatch addresses both issues simul- tancously, achieving higher data utilization and more reliable pseudo-label accuracy.
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Published as a conference paper at ICLR 2026
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Figure 11: Comparison of utilization rate and pseudo-label accuracy of OpenMatch (Saito et al, 202T). SSB (Fan et al. 2023) and FedOpenMatchh (ours), which share the same architecture. Open- Match suffers from both a low data utilization rate and poor pseudo-label quality, as most samples mistakenly identified as outliers by the imbalanced OVA classifier, thereby limiting its perfor- mance. SSB relaxes the pscudo-label filtering strategy to increase data utilization, but this introduces a large number of incorrect pseudo-labels. In contrast, FedOpenMatch addresses both issues simul- taneously, achieving higher data utilization and more reliable pseudo-label accuracy.
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Figure 12: Performance comparison of FedOpenMatch under different OVA thresholds on the CIFAR-100 dataset. The results show that closed-set accuracy remains stable across wide range of OVA thresholds, indicating that this component has limited impact on inlier classification. In contrast, open-set accuracy is more sensitive to the threshold value. A low threshold admits more samples during training but also introduces substantial noise, leading to unstable optimization and degraded open-set performance. Therefore, using higher threshold is generally preferable, as filters out unreliable samples and stabilizes training.
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Published as a conference paper at ICLR 2026
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Figure 13: Performance comparison under complex settings where label shift and feature shift coex- ist. We further evaluate FedOpenMatch in a more realistic scenario that introduces both label shift and feature shift simultaneously. We compare against FedLabel and 1OMatch, the two strongest- performing baselines. Across all metries, FedOpenMatch maintains clear performance margin over both methods, demonstrating its robustness and superiority under challenging, real- worldworld data heterogeneity.
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Figure 14: Performance comparison under extreme scenarios with high unknown-class prevalence. We further evaluate FedOpenMatch CIFAR- 100 with only 20 seen classes and 80 unseen classes, creating an extreme setting where unknown samples dominate the data. Even in this highly chal- lenging scenario, FedOpenMatch consistently achieves the best performance among all methods, demonstrating strong robustness and clear superiority under extreme open-set conditions.
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Reviews:
[{"review_id": "jUhs7HNJPG", "reviewer": "Reviewer_jhAD", "summary": "The paper introduces **FedOpenMatch**, the first framework for **Open-Set Semi-Supervised Federated Learning (OSSFL)**—a realistic yet previously unexplored setting where unlabeled client data contain **outliers**. Unlike standard SSFL methods that assume closed-set label spaces, FedOpenMatch jointly trains an **inlier classifier** and a **one-vs-all (OVA) outlier detector** to safely leverage open-set unlabeled data.  Extensive experiments on CIFAR-10, CIFAR-100, and SVHN show FedOpenMatch significantly boosts.", "questions": "Does your method work with bigger models or more realistic data splits?\n\nAll experiments use ResNet-18 and synthetic data splits (Dirichlet). But real-world data may have domain shifts (e.g., different hospitals or cameras), and people now often use Vision Transformers (ViTs).  \n- Have you tried FedOpenMatch with a ViT or a more realistic non-IID split (e.g., by domain or semantic group)?  \n- Would the “gradient stop” still be needed in those cases, or is it only helpful for ResNet-18?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper demonstrates good **originality** by formally defining a new and realistic problem setting—Open-Set Semi-Supervised Federated Learning (OSSFL)—which bridges the gap between open-set semi-supervised learning and federated learning. The experimental design is also **rigorous**, covering multiple datasets, label scarcity regimes, and heterogeneity levels, with thorough ablation studies validating each component. The writing is clear and well-structured.", "weaknesses": "1. The method assumes the server’s labeled set is perfectly balanced and clean, which may not hold in real-world label-at-server settings; robustness to label noise or skewed class priors is unexamined. \n\n2. Second, all experiments use ResNet-18 and synthetic Dirichlet splits—evaluations on larger models (e.g., ViTs) may change the conclusion on feature conflicts between the OVA classifier and the inlier classifier.", "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": "", "extra_scores": {}}, {"review_id": "MJie6uOiWF", "reviewer": "Reviewer_ikNd", "summary": "This paper defines Open-set Semi-Supervised Federated Learning (OSSFL) (unlabeled client data contain unseen classes) and presents FedOpenMatch, the first framework for this setting. It combines an OVA outlier detector with logit adjustment for imbalance, a gradient-stop to decouple OVA/inlier heads, and logit consistency regularization. Experiments on standard benchmarks show sizable improvements in open-set accuracy (e.g., +14.33% on CIFAR-100).", "questions": "- How sensitive is OVA performance to class imbalance and thresholds under client heterogeneity?\n- Can gradient-stop harm representation sharing between inlier/OVA? Any alternatives (e.g., orthogonal heads)?\n- What happens when unknown prevalence is very high or client pools have disjoint seen sets?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- First formalization of OSSFL and a tailored framework\n- Strong, consistent gains; well-motivated components (OVA + adjustment + gradient-stop + consistency)\n- Good figures/tables and taxonomy (SSFL vs. OSSL vs. FOSR vs. OSSFL).\n- Addresses a real-world failure mode for SSFL; likely baseline for future work.", "weaknesses": "- Limited analysis of non-IID client shifts (class/feature distribution) on OVA calibration.\n- Communication/compute overhead of OVA and consistency losses isn’t profiled; end-to-end systems analysis would help.\n- Robustness to extreme open-set ratios and ablations isolating each component could be expanded.", "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": "", "extra_scores": {}}, {"review_id": "inmp0lYMov", "reviewer": "Reviewer_5NdG", "summary": "The paper introduces the open-set semi-supervised federated learning (OSSFL) problem, where clients possess unlabeled data potentially containing unseen classes, and the server maintains a small labeled set. To address this, the authors propose FedOpenMatch, a framework that jointly trains an inlier classifier and a one-vs-all (OVA) outlier detector. The method incorporates gradient stop, logit adjustment, and logit consistency regularization to improve stability and open-set discrimination under federated conditions.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper presents a clear problem setting that distinguishes SSFL, OSSL, FOSR, and the OSSFL. By adding the GS, logit adjustment and LCR to a standard architecture, experimental results show FedOpenMatch improves both closed-set and open-set balanced accuracy over federated adaptations of OSSL baselines and standard SSFL methods.", "weaknesses": "While the paper positions as a new OSSFL formulation, its novelty relative to prior open-world SSFL work is limited and largely rooted in scenario framing rather than a new learning principle. The authors should better justify why the label-at-server configuration represents the dominant or more practical case, and clarify how insights generalize across settings. As it stands, the novelty appears to hinge more on scenario framing than on a fundamentally new learning principle.\n\nAdditionally, centralized OSSL algorithms appear to be adapted to FL. Their relatively weak and unstable performance raises concern that implementations or hyperparameters under FL are suboptimal. More evidence could be helpful to show these are strong federated instantiations rather than strawmen.\n\nFurther, the evaluation setup is narrow, using only ResNet-18, limited Dirichlet heterogeneity, and no covariate or mixed-shift scenarios. Key design details, such as pseudo-label staleness under non-IID drift, are under-analyzed. Finally, grouping all unseen classes into a single \"unknown\" category obscures variability.", "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": "", "extra_scores": {}}, {"review_id": "0DFIaYKsPC", "reviewer": "Reviewer_abnT", "summary": "This paper proposes a method called FedOpenMatch for open-set federated semi-supervised learning. By \"open-set\", the authors refer to the setting where outlier classes exist in unlabeled datasets, and the model should be able to classify inlier classes right, while detecting outlier classes. To address the problem, FedOpenMatch leverages a two-head structure, with a feature extractor, an inlier classifier and an OVA outlier classifier. The authors introduce several techniques to enhance the performance of FedOpenMatch, such as stop-gradients for the OVA branch, logit consistency instead of probability consistency, and logit adjustment to handle the imbalance issue of OVA classifiers. Experiments are done on three datasets and against multiple baselines, where FedOpenMatch outperforms a variety of baselines, including open-set and closed-set ones.", "questions": "One minor question about FedOpenMatch is when a sample will be categorized as outlier, e.g. when all OVA classifiers report it as an outlier? I am not highly familiar with open-set semi-supervised learning so some additional backgrounds may help.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper tackles the problem of open-set federated semi-supervised learning, which is of practical value. Being able to detect outlier classes is an important property especially in FL, where the collected data may contain a large amount of noise. \n2. This paper is overall well-written and easy to follow. The organization of this paper is reasonable, and the technical solutions and intuitions are clearly stated together, making it easy to understand. \n3. Experiments of FedOpenMatch is extensive. The authors compare against lots of baselines, including SSFL algorithms and adapted Open-set SSL methods (to the FL setting) with multiple baselines, number of seen classes, etc. Moreover, ablation studies are sufficient and demonstrate the impact of each individual design technique. Some ablation studies even analyze why the proposed techniques help (e.g. analyzing gradient similarity to understand the impact of gradient stop). This makes the paper well-justified.", "weaknesses": "1. One major weakness of this paper is that it is built upon multiple existing ideas. For example, the logit adjustment method is directly taken from Menon et al. 2021, and the weak-strong consistency is modified from existing methods. Overall, this slightly weakens the amount of novel insights of this paper, despite still being a solid paper with new problems tackled. \n2. I am interested in how FedOpenMatch is sensitive to the number of local iterations/epochs. As local clients only have unlabeled data, training on solely unlabeled data for too long may lead to diverging model updates. Therefore, from my understanding, the number of local iterations/epochs is an important parameter to determine in federated SSL. The authors did not provide such analyses though. \n3. Gradient similarity fail to completely explain the accuracy curve. While in Figure 3, the relations between gradient similarity and open-set accuracy is significant, such relations are not so apparent in Figures 4 and 5. For example, in Figure 4, for the early ~250 steps, gradient similarity is below 0.2, yet the accuracies are close. This makes me wonder whether there are other factors that may impact the accuracy, e.g. gradient magnitude.  \n4. Minor points. \n    - It seems that in Eqn. 6, the sign of $\\omega\\log \\pi$ is reversed. I checked the original paper and found that the sign before the term should be - instead of +.", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)