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Evidence record 3373 · automatically gathered

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 limi

Record details

Published: 31 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Safety & alignment · Privacy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (31 May 2026), “Silent Failures in Federated Personalization of Foundation Models,” evidence record 3373, https://ethics.ai/record/3373 (originally published by arXiv).

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