Silent Failures in Federated Personalization of Foundation Models 事件

Silent Failures in Federated Personalization of Foundation Models arXiv:2606.00947v1 Announce Type: cross Abstract: Foundation models are increasingly personalized on decentralized private data through federated learning and are now deployed at scale under growing regulatory requirements for post-market monitoring. We argue that this convergence creates a distinct and under-recognized class of trustworthiness failures, which we term "Silent Failures." These include amplified bias, fairness coll