GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization

• URL: http://arxiv.org/abs/2108.13858v1
• Date: Tue, 31 Aug 2021 14:09:04 GMT
• Title: GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
• Authors: Yen-Hsiu Chou, Shenda Hong, Chenxi Sun, Derun Cai, Moxian Song, Hongyan Li
• Abstract summary: We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Our results show that our GRP-FED improves under both global and local scenarios.
• Score: 6.592268037926868
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to train across decentralized clients as practical applications. We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue by considering a single global model and multiple local models for each client. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Each local model is learned from the local data and aligns with its distribution for customization. To prevent the local model from just overfitting, GRP-FED applies an adversarial discriminator to regularize between the learned global-local features. Extensive results show that our GRP-FED improves under both global and local scenarios on real-world MIT-BIH and synthesis CIFAR-10 datasets, achieving comparable performance and addressing client imbalance.

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