Quantization-Tolerant Gradient Auditing for Active Inference-Attack Detection in Federated Learning

Reported by arXiv cs.CR (Michael Kuehne-Schlinkert)

Summary by Cyber Security News12 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.13311

arXiv:2610.13311v1 Announce Type: new Abstract: Inside a federated learning system a participant can quietly study the shared model and learn about others' private data. The usual defense, differential privacy, often adds so much noise that the model is no longer worth training. In cross-silo FL, where participants are a few accountable organizations and the server is trusted, we ask whether the server can instead interrogate clients.

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