AI RESEARCH
Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
arXiv CS.LG
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ArXi:2605.16438v1 Announce Type: new Federated Learning (FL) trains a global model across decentralized clients while preserving data privacy, but at scale it is vulnerable to malicious updates. Byzantine-resilient aggregation methods such as MultiKrum score gradients against their nearest neighbors and can miss malicious updates that preserve the statistical properties of honest ones.