mlsanityData to 7 Oct 2026

Federated Mutual Learning

arXiv 2006.16765 ↗First posted 27 Jun 2020cs.LGcited by 70

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Abstract (arXiv)

Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about distinctive challenges to the canonical federated learning algorithm (FedAvg). First, due to the Non-IIDness of data, the global shared model may perform worse than local models that solely trained on their private data; Second, the objective of center server and clients may be different, where center server seeks for a generalized model whereas client pursue a personalized model, and clients may run different tasks; Third, clients may need to design their customized model for various scenes and tasks; In this work, we present a novel federated learning paradigm, named Federated Mutual Leaning (FML), dealing with the three heterogeneities. FML allows clients training a generalized model collaboratively and a personalized model independently, and designing their private customized models. Thus, the Non-IIDness of data is no longer a bug but a feature that clients can be personally served better. The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 9; 2023: 19; 2024: 19; 2025: 5; 2026: 8.

70 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  3. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 62
  4. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 56
  5. 2023FedGH: Heterogeneous Federated Learning with Generalized Global Headercited by 56
  6. 2024FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learningcited by 47
  7. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  8. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 39
  9. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  10. 2021QuPeD: Quantized Personalization via Distillation with Applications to Federated Learningcited by 32
  11. 2021Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learningcited by 25
  12. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
38 more
  1. 2024Federated Model Heterogeneous Matryoshka Representation Learningcited by 21
  2. 2024Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matchingcited by 21
  3. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  4. 2021FedH2L: Federated Learning with Model and Statistical Heterogeneitycited by 17
  5. 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learningcited by 17
  6. 2023PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmarkcited by 16
  7. 2023Universal Domain Adaptation via Compressive Attention Matchingcited by 15
  8. 2021FedHe: Heterogeneous Models and Communication-Efficient Federated Learningcited by 13
  9. 2022FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networkscited by 12
  10. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  11. 2023InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representationcited by 11
  12. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  13. 2021FedMe: Federated Learning via Model Exchangecited by 10
  14. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  15. 2023FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learningcited by 10
  16. 2024Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architecturescited by 10
  17. 2023pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharingcited by 9
  18. 2025FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learningcited by 8
  19. 2023Generalized Universal Domain Adaptation with Generative Flow Networkscited by 7
  20. 2023Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learningcited by 7
  21. 2025HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmarkcited by 7
  22. 2022An Empirical Study of Personalized Federated Learningcited by 6
  23. 2021Decentralised Person Re-Identification with Selective Knowledge Aggregationcited by 5
  24. 2022Decentralized Learning with Multi-Headed Distillationcited by 5
  25. 2023Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learningcited by 5
  26. 2023Leveraging Foundation Models to Improve Lightweight Clients in Federated Learningcited by 5
  27. 2025Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learningcited by 5
  28. 2021Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenescited by 4
  29. 2022Avoid Overfitting User Specific Information in Federated Keyword Spottingcited by 4
  30. 2024DFML: Decentralized Federated Mutual Learningcited by 4
  31. 2024pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learningcited by 3
  32. 2024The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learningcited by 3
  33. 2026STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learningcited by 3
  34. 2022A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacycited by 2
  35. 2024Fairness Auditing with Multi-Agent Collaborationcited by 2
  36. 2025Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environmentscited by 2
  37. 2022Personalized Federated Learning with Hidden Information on Personalized Priorcited by 1
  38. 2023FedIN: Federated Intermediate Layers Learning for Model Heterogeneitycited by 1

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