mlsanityData to 14 Oct 2026

FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data

arXiv 2404.17916 ↗First posted 27 Apr 2024cs.LG cs.AIcited by 3

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Abstract (arXiv)

Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed computing. To deal with it, this paper proposes a novel personalized federated learning algorithm, named Federated Contrastive Shareable Representations (FedCoSR), to facilitate knowledge sharing among clients while maintaining data privacy. Specifically, the parameters of local models' shallow layers and typical local representations are both considered as shareable information for the server and are aggregated globally. To address performance degradation caused by label distribution skew among clients, contrastive learning is adopted between local and global representations to enrich local knowledge. Additionally, to ensure fairness for clients with scarce data, FedCoSR introduces adaptive local aggregation to coordinate the global model involvement in each client. Our simulations demonstrate FedCoSR's effectiveness in mitigating label heterogeneity by achieving accuracy and fairness improvements over existing methods on datasets with varying degrees of label heterogeneity.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 32 of its 63 references that are in the map. First posted on arXiv 27 Apr 2024.

Builds on

32 of 63 references.

References · 32

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20 more
  1. 2021Towards Personalized Federated Learningcited by 464
  2. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  3. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  4. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  5. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  6. 2021Personalized Federated Learning using Hypernetworkscited by 212
  7. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  8. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  9. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  10. 2021Gradient Inversion with Generative Image Priorcited by 101
  11. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  12. 2023Multimodal Federated Learning via Contrastive Representation Ensemblecited by 86
  13. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 81
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  15. 2023FedGH: Heterogeneous Federated Learning with Generalized Global Headercited by 56
  16. 2024Relaxed Contrastive Learning for Federated Learningcited by 32
  17. 2023PeFLL: Personalized Federated Learning by Learning to Learncited by 21
  18. 2021FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximationcited by 10
  19. 2023Federated Few-shot Learningcited by 9
  20. 2023FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classificationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Federated Learning with Heterogeneous and Private Label Setscited by 0
  2. 2026Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clientscited by 0
  3. 2026Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Frameworkcited by 0