PoCoFL: POlicy-COmpliant Federated Learning
[Kryptografia i bezpieczeństwo] Autorzy: Dominik Roy George, Varesh Mishra, Aysajan Abidin
Federated Learning (FL) is a privacy-oriented learning paradigm that enables collaborative model training while keeping training data local to participating clients. However, it does not guarantee that clients submit policy-compliant contributions or that aggregators process admitted contributions correctly. Existing verifiable FL systems tailor validation rules to specific FL settings, learning workflows, and cryptographic constructions, limiting their applicability across network topologies, participant roles, and aggregation semantics. In this paper, we present PoCoFL, a policy-compliant federated learning framework that separates three aspects: (i) FL type, (ii) policy semantics, and (iii) cryptographic realisation. We provide a formalisation that captures client and aggregation requirements as policy-dependent relations. Clients prove compliance of their contributions using commitm…
- Kategoria arxiv
- cs.CR
- Kategoria
- Kryptografia i bezpieczeństwo