Silent Failures in Federated Personalization of Foundation Models
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 collapse, and alignment erosion that may remain difficult to detect because federated learning's privacy constraints limit visibility into model behavior. A landscape analysis of existing benchmarks reveals a structural divide. Federated benchmarks evaluate system performance but provide limited insight into model be...
| Authors | YongKyung Oh, Alex Bui |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 11 June 2026 |
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