Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems
Cross-domain federated learning framework pairs differential privacy with Byzantine-robust aggregation for banking and healthcare — directly relevant to DORA-regulated institutions exploring collaborative AI without data pooling.
Summary written by editorial AI · Source link below
arXiv:2609.03064v1 Announce Type: new Abstract: Federated learning allows banks, hospitals, and other regulated organizations to train a shared model without moving raw records off their own servers, which is attractive wherever data protection law or competitive sensitivity rules out pooling data centrally. Two problems limit how far this promise can be trusted in practice. First, the parameter updates that clients exchange still leak information about local records through gradient inversion
Editorial Analysis
DORA-regulated financial institutions exploring federated ML face the dual challenge of privacy preservation and adversarial robustness; this framework addresses both simultaneously.
Evaluate this combined DP + Byzantine-robust FL approach as a candidate architecture for cross-institutional model training under DORA constraints.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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