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DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning

arXiv 2403.09284 ↗First posted 14 Mar 2024cs.LG cs.DCcited by 0

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Abstract (arXiv)

Personalized federated learning becomes a hot research topic that can learn a personalized learning model for each client. Existing personalized federated learning models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. However, similaritybased personalized federated learning methods may exacerbate the class imbalanced problem. In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning. Specifically, we build an affinity metric from a complementary perspective to guide which clients should be aggregated. Then we design a dynamic aggregation strategy to dynamically aggregate clients based on the affinity metric in each round to reduce the class imbalanced risk. Extensive experiments show that the proposed DA-PFL model can significantly improve the accuracy of each client in three real-world datasets with state-of-the-art comparison methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 42 of its 73 references that are in the map. First posted on arXiv 14 Mar 2024.

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References · 42

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30 more
  1. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
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  9. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 95
  10. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
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  24. 2022Differentially Private Federated Learning with Local Regularization and Sparsificationcited by 35
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  26. 2021Fast-adapting and Privacy-preserving Federated Recommender Systemcited by 30
  27. 2022Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attackscited by 21
  28. 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
  29. 2021PFA: Privacy-preserving Federated Adaptation for Effective Model Personalizationcited by 18
  30. 2022Fast Heterogeneous Federated Learning with Hybrid Client Selectioncited by 8

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