让参与联邦学习的客户端能匿名证明贡献，兼顾隐私与可审计性。
FedPoP: Federated Learning Meets Proof of Participation
- 基于安全聚合协议，实现无追踪的参与证明机制。
- 每轮额外开销仅0.97秒，证明贡献耗时0.0612秒。
- 适合需要可信贡献记录的隐私保护场景，如数字资产训练。
联邦学习（FL）提供了一种保护隐私的分布式机器学习方式，使客户端可在不共享本地数据的情况下参与全局模型训练。随着模型逐渐成为可货币化的数字资产，证明参与训练变得至关重要，以确立所有权。本文提出FedPoP，一种新型联邦学习框架，可在不暴露客户端身份的前提下，实现不可链接的参与证明，且无需复杂计算或公共账本。该框架可无缝集成至现有安全聚合协议中，确保与真实世界部署兼容。我们实现了概念验证并进行了真实环境下客户端掉线情况下的实证评估。原型系统显示，FedPoP在安全聚合基础上每轮增加0.97秒开销，客户端向第三方证明其贡献仅需0.0612秒。结果表明，该方案在保持隐私的同时具备实际部署可行性。
原文摘要 · Abstract (English)
Federated learning (FL) offers privacy preserving, distributed machine learning, allowing clients to contribute to a global model without revealing their local data. As models increasingly serve as monetizable digital assets, the ability to prove participation in their training becomes essential for establishing ownership. In this paper, we address this emerging need by introducing FedPoP, a novel FL framework that allows nonlinkable proof of participation while preserving client anonymity and privacy without requiring either extensive computations or a public ledger. FedPoP is designed to seamlessly integrate with existing secure aggregation protocols to ensure compatibility with real-world FL deployments. We provide a proof of concept implementation and an empirical evaluation under realistic client dropouts. In our prototype, FedPoP introduces 0.97 seconds of per-round overhead atop securely aggregated FL and enables a client to prove its participation/contribution to a model held by a third party in 0.0612 seconds. These results indicate FedPoP is practical for real-world deployments that require auditable participation without sacrificing privacy.
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