GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework 事件

GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework arXiv:2504.17471v2 Announce Type: replace-cross Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers. Recent approaches rely on dynamic communication graphs built using Random Peer Sampling (RPS) protocols which have been proven to accelerate convergence. However, we show that these approaches are vulnerable to a dual attack