End-to-End Verifiable and Robust Federated Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2607. 06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy.
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:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
arXiv:2608.21137v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...
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.
arXiv:2410. 11378v3 Announce Type: replace-cross Abstract: Personalized collaborative learning in federated settings faces a critical trade-off between customization and participant trust.