A Coalition Formation Game Approach for Personalized Federated Learning
- URL: http://arxiv.org/abs/2202.02502v2
- Date: Tue, 8 Feb 2022 05:05:37 GMT
- Title: A Coalition Formation Game Approach for Personalized Federated Learning
- Authors: Leijie Wu, Song Guo, Yaohong Ding, Yufeng Zhan, Jie Zhang
- Abstract summary: We propose a novel personalized algorithm: pFedSV, which can 1. identify each client's optimal collaborator coalition and 2. perform personalized model aggregation based on SV.
The results show that pFedSV can achieve superior personalized accuracy for each client, compared to the state-of-the-art benchmarks.
- Score: 12.784305390534888
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Facing the challenge of statistical diversity in client local data
distribution, personalized federated learning (PFL) has become a growing
research hotspot. Although the state-of-the-art methods with model
similarity-based pairwise collaboration have achieved promising performance,
they neglect the fact that model aggregation is essentially a collaboration
process within the coalition, where the complex multiwise influences take place
among clients. In this paper, we first apply Shapley value (SV) from coalition
game theory into the PFL scenario. To measure the multiwise collaboration among
a group of clients on the personalized learning performance, SV takes their
marginal contribution to the final result as a metric. We propose a novel
personalized algorithm: pFedSV, which can 1. identify each client's optimal
collaborator coalition and 2. perform personalized model aggregation based on
SV. Extensive experiments on various datasets (MNIST, Fashion-MNIST, and
CIFAR-10) are conducted with different Non-IID data settings (Pathological and
Dirichlet). The results show that pFedSV can achieve superior personalized
accuracy for each client, compared to the state-of-the-art benchmarks.
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