# PoCoFL: POlicy-COmpliant Federated Learning

arXiv: https://arxiv.org/abs/2610.03650 · HTML: https://mlsanity.com/paper/2610.03650/

## 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.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Continual Learning with Weighted Inter-client Transfer](https://mlsanity.com/paper/2003.03196/) (2020)
- [Byzantine-Robust Decentralized Federated Learning](https://mlsanity.com/paper/2406.10416/) (2024)
- [EIFFeL: Ensuring Integrity for Federated Learning](https://mlsanity.com/paper/2112.12727/) (2021)
- [RoFL: Robustness of Secure Federated Learning](https://mlsanity.com/paper/2107.03311/) (2021)
- [Elastic Weight Consolidation (EWC): Nuts and Bolts](https://mlsanity.com/paper/2105.04093/) (2021)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
- [Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning](https://mlsanity.com/paper/2011.12623/) (2020)
- [Zero-Knowledge Proof-based Practical Federated Learning on Blockchain](https://mlsanity.com/paper/2304.05590/) (2023)
- [Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs](https://mlsanity.com/paper/2311.15310/) (2023)
- [No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning](https://mlsanity.com/paper/2406.01080/) (2024)
- [Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2407.19703/) (2024)
- [VerifiableFL: Verifiable Claims for Federated Learning using Exclaves](https://mlsanity.com/paper/2412.10537/) (2024)
- [Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats](https://mlsanity.com/paper/2509.00634/) (2025)
- [Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing](https://mlsanity.com/paper/2509.08709/) (2025)

## 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.