On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach 事件

On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach arXiv:2605.26162v1 Announce Type: cross Abstract: Asynchronous decentralized federated learning (ADFL) eliminates central coordination and global synchronization, making it attractive for large-scale and heterogeneous systems. However, frequent peer-to-peer communication, asynchronous updates on directed topologies, and non-IID data jointly lead to excessive communication overhead, biased aggregation and s