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arXiv Machine Learning By Hangli Ge, Noboru Koshizuka

Extending FAIR Principles with Observability for Trustworthy Federated Learning

Read the original on arXiv Machine Learning →

The paper proposes extending the FAIR principles with an observability layer—FAIR-O—to enhance trust in federated learning. It argues that merely keeping data local is insufficient for trustworthiness, and introduces a framework where each participant generates signed, minimal trust evidence for every FL round, while raw data and logs stay local. A prototype demonstrates evidence generation and a three‑level trust model, and the authors outline how this evidence could inform participant selection and aggregation in future work.

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