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

arXiv:2605.26162v1 Announce Type: new 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 severe model drift. We propose PushCen-ADFL, a communication-efficient ADFL framework that enables stable training under asymmetric communication and delayed client participation. PushCen-ADFL c

Source: arXiv cs.LG — read the full report at the original publisher.

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