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Cross-Fusion Rule for Personalized Federated Learning

arXiv 2302.02531 ↗First posted 6 Feb 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Data scarcity and heterogeneity pose significant performance challenges for personalized federated learning, and these challenges are mainly reflected in overfitting and low precision in existing methods. To overcome these challenges, a multi-layer multi-fusion strategy framework is proposed in this paper, i.e., the server adopts the network layer parameters of each client upload model as the basic unit of fusion for information-sharing calculation. Then, a new fusion strategy combining personalized and generic is purposefully proposed, and the network layer number fusion threshold of each fusion strategy is designed according to the network layer function. Under this mechanism, the L2-Norm negative exponential similarity metric is employed to calculate the fusion weights of the corresponding feature extraction layer parameters for each client, thus improving the efficiency of heterogeneous data personalized collaboration. Meanwhile, the federated global optimal model approximation fusion strategy is adopted in the network full-connect layer, and this generic fusion strategy alleviates the overfitting introduced by forceful personalized. Finally, the experimental results show that the proposed method is superior to the state-of-the-art methods.

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References · 22

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10 more
  1. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  2. 2021Personalized Federated Learning with Gaussian Processescited by 76
  3. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
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  5. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  6. 2021Federated Learning with Taskonomy for Non-IID Datacited by 21
  7. 2022FedStack: Personalized activity monitoring using stacked federated learningcited by 15
  8. 2020Federated Block Coordinate Descent Scheme for Learning Global and Personalized Modelscited by 11
  9. 2021Privacy-preserving Federated Learning for Residential Short Term Load Forecastingcited by 4
  10. 2022FedMCSA: Personalized Federated Learning via Model Components Self-Attentioncited by 3

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