FedPoP: federated learning meets proof of participation

Abstract

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 non-linkable 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 adds lightweight proof-generation and verification overhead on top of securely aggregated FL and enables a client to prove participation to a service provider in 0.0612 s. These results indicate FedPoP is practical for real-world deployments that require auditable participation without sacrificing privacy.