Paper recorded by Signals 4 on 2026-09-09 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10
Category: cs.LG · 机器学习 · first seen 2026-09-10
Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often dominate round completion time and exacerbate the straggler effect. Hybrid FL addresses this challenge by combining synchronous and asynchronous client participation, but effective