PoCoFL: POlicy-COmpliant Federated Learning
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 ...
| Authors | Dominik Roy George, Varesh Mishra, Aysajan Abidin |
|---|---|
| Primary category | cs.CR |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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