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integrity risks

topic1 events
papersTODAY 04:00 UTC

arXiv paper proposes end-to-end verifiable and robust federated learning

A new arXiv preprint examines integrity risks in federated learning, where an aggregator coordinates training across parties without pooling raw data. The authors argue that once participants or infrastructure cannot be fully trusted, additional guarantees are needed, and they outline two requirements their approach aims to satisfy. The work targets end-to-end verification alongside robustness for the federated setting.

federated-learning/arxiv/privacy/robustness