mlsanityData to 7 Oct 2026

Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

arXiv 2204.03304 ↗First posted 7 Apr 2022cs.LGcited by 20

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Abstract (arXiv)

Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the applicability of FL in practice. In this paper, we show the possibility of unsupervised FL whose model is still a classifier for predicting class labels, if the class-prior probabilities are shifted while the class-conditional distributions are shared among the unlabeled data owned by the clients. We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model. FedUL is a very general solution to unsupervised FL: it is compatible with many supervised FL methods, and the recovery of the wanted model can be theoretically guaranteed as if the data have been labeled. Experiments on benchmark and real-world datasets demonstrate the effectiveness of FedUL. Code is available at https://github.com/lunanbit/FedUL.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 7 Apr 2022.

Builds on

24 of 45 references.

References · 24

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  7. 2019Advances and Open Problems in Federated Learningcited by 3,021
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  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  10. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  11. 2019Federated Learning with Personalization Layerscited by 533
  12. 2020An Efficient Framework for Clustered Federated Learningcited by 505
12 more
  1. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  2. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  3. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  4. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  5. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  6. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  7. 2019Differentially Private Meta-Learningcited by 40
  8. 2014On Learning from Label Proportionscited by 38
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  12. 2021Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classificationcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 6; 2024: 4; 2025: 2; 2026: 1.

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  1. 2022Orchestra: Unsupervised Federated Learning via Globally Consistent Clusteringcited by 45
  2. 2022Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learningcited by 35
  3. 2023Adaptive Test-Time Personalization for Federated Learningcited by 23
  4. 2022Federated Self-supervised Learning for Heterogeneous Clientscited by 16
  5. 2022Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?cited by 12
  6. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  7. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  8. 2024A Mutual Information Perspective on Federated Contrastive Learningcited by 7
  9. 2023Multi-Source to Multi-Target Decentralized Federated Domain Adaptationcited by 6
  10. 2022Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchangecited by 5
  11. 2024Lightweight Unsupervised Federated Learning with Pretrained Vision Language Modelcited by 5
  12. 2023Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labelscited by 4
8 more
  1. 2023Masked Autoencoders are Efficient Continual Federated Learnerscited by 1
  2. 2023UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Datacited by 1
  3. 2022FedMT: Federated Learning with Mixed-type Labelscited by 0
  4. 2023A Quality-of-Service Compliance System using Federated Learning and Optimistic Rollupscited by 0
  5. 2024Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labelscited by 0
  6. 2025FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computingcited by 0
  7. 2025Cooperative Pseudo Labeling for Unsupervised Federated Classificationcited by 0
  8. 2026When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learningcited by 0