mlsanityData to 14 Oct 2026

PoCoFL: POlicy-COmpliant Federated Learning

arXiv 2610.03650 ↗First posted 2 Oct 2026cs.CR cs.LGcited by 0

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Abstract (arXiv)

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 commitments and non-interactive zero-knowledge proofs, while aggregators prove that the recorded set of admitted contributions was processed according to the selected aggregation policy. We demonstrate PoCoFL through four formal instantiations: (i) vanilla, (ii) continual, (iii) personalised, and (iv) threshold-encrypted federated learning. We evaluate the effects of policy enforcement on the learning objectives of vanilla, personalised, and continual FL. We further implement proof-of-concept realisations of all four instantiations, demonstrating the versatility and practical feasibility of PoCoFL. Overall, these results show that PoCoFL can capture complex policy representations while remaining network-topology agnostic.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 44 references that are in the map. First posted on arXiv 2 Oct 2026.

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References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2018How To Backdoor Federated Learningcited by 765
  4. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  6. 2020Federated Continual Learning with Weighted Inter-client Transfercited by 161
  7. 2024Byzantine-Robust Decentralized Federated Learningcited by 48
  8. 2021EIFFeL: Ensuring Integrity for Federated Learningcited by 28
  9. 2021RoFL: Robustness of Secure Federated Learningcited by 25
  10. 2021Elastic Weight Consolidation (EWC): Nuts and Boltscited by 23
  11. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 22
  12. 2020Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learningcited by 13
7 more
  1. 2023Zero-Knowledge Proof-based Practical Federated Learning on Blockchaincited by 10
  2. 2023Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofscited by 5
  3. 2024No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learningcited by 3
  4. 2024Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learningcited by 2
  5. 2024VerifiableFL: Verifiable Claims for Federated Learning using Exclavescited by 1
  6. 2025Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threatscited by 1
  7. 2025Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditingcited by 1

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