发表机构
KU Leuven（鲁汶大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PoCoFL提出一种策略合规的联邦学习框架，分离FL类型、策略语义与密码学实现，通过承诺与零知识证明实现客户端贡献和聚合过程的合规验证，支持多种FL变体，且网络拓扑无关。
AI 中文摘要
联邦学习（FL）是一种面向隐私的学习范式，能够在保持训练数据位于参与客户端本地的同时，实现协作式模型训练。然而，它并不保证客户端提交的策略合规贡献，也不保证聚合器正确处理已接受的贡献。现有的可验证联邦学习系统将验证规则定制于特定的FL设置、学习工作流和密码学构造，限制了其在不同网络拓扑、参与者角色和聚合语义中的适用性。在本文中，我们提出了PoCoFL，一个策略合规的联邦学习框架，它将三个方面分离：（i）FL类型，（ii）策略语义，以及（iii）密码学实现。我们提供了一种形式化方法，将客户端和聚合需求捕获为策略依赖的关系。客户端使用承诺和非交互式零知识证明来证明其贡献的合规性，而聚合器则证明所记录的已接受贡献集已根据所选聚合策略进行处理。我们通过四个形式化实例来展示PoCoFL：（i）vanilla（基础）、（ii）continual（持续）、（iii）personalised（个性化）以及（iv）threshold-encrypted（阈值加密）联邦学习。我们评估了策略执行对vanilla、个性化及持续FL学习目标的影响。我们进一步实现了所有四种实例的概念验证实现，展示了PoCoFL的多功能性和实际可行性。总体而言，这些结果表明PoCoFL能够捕获复杂的策略表示，同时保持网络拓扑无关性。
英文摘要
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.
