Hugging Face Trending Papers
Jul 7

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks.

arXiv Machine Learning
Oct 5

PoCoFL: POlicy-COmpliant Federated Learning

PoCoFL is a federated learning framework that separates the type of FL, policy semantics, and cryptographic realization to ensure policy compliance. It formalizes client and aggregation requirements as policy-dependent relations, allowing clients to prove compliance with commitments and zero-knowledge proofs, and aggregators to prove correct processing of admitted contributions. The paper demonstrates PoCoFL through four instantiations—vanilla, continual, personalized, and threshold-encrypted FL—showing its versatility and practical feasibility across different learning objectives.

By Dominik Roy George, Varesh Mishra, Aysajan Abidin