所属单位：

AAAI Press

发表刊物：

Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023

摘要：

Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularization in local optimization or improving the model aggregation scheme on the server. However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To address this issue, we propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the "one-vs-all" training strategy in each client to alleviate the unfair competition between classes by constructing a personalized binary classification problem for each class. This may aggravate the class imbalance challenge and thus a novel personalized binary classification loss that incorporates both the under-sampling and hard sample mining strategies is designed. Extensive experiments are conducted on two popular datasets under different settings, and the results demonstrate that our FedABC can significantly outperform the existing counterparts.

合写作者：

Shen Li,Luo Yong,Hu Han,Wen Yonggang,Tao Dacheng

第一作者：

Wang Dui

论文类型：

期刊论文

通讯作者：

Su Kehua

文献类型：

J

卷号：

37

页面范围：

10095-10103

是否译文：

否

发表时间：

2023-06-23

收录刊物：

EI