AI RESEARCH
Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resilience
arXiv CS.LG
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ArXi:2508.00596v4 Announce Type: replace-cross In decentralized federated learning (FL), multiple clients collaboratively learn a shared machine learning (ML) model by leveraging their privately held datasets distributed across the network, through interactive exchange of the intermediate model updates. To ensure data security, cryptographic techniques are commonly employed to protect model updates during aggregation.