Heterogeneity-Aware Federated Learning

Yang Chengxu
Yang Chengxu
Wang QiPeng
Wang QiPeng
Wang Shangguang
Wang Shangguang
Liu Xuanzhe
Liu Xuanzhe
Cited by: 0|Views32

Abstract:

Federated learning (FL) is an emerging distributed machine learning paradigm that stands out with its inherent privacy-preserving advantages. Heterogeneity is one of the core challenges in FL, which resides in the diverse user behaviors and hardware capacity across devices who participate in the training. Heterogeneity inherently exerts...More

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