FedPoP: Federated Learning Meets Proof of Participation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 11 Nov 2025.
Builds on
17 of 37 references.
References · 17
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
- 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
- 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
- 2022The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retrainingcited by 109
- 2020WAFFLE: Watermarking in Federated Learningcited by 34
- 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 33
- 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
- 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learningcited by 16
- 2022Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooringcited by 14
- 2023FedZKP: Federated Model Ownership Verification with Zero-knowledge Proofcited by 9
- 2023FedCIP: Federated Client Intellectual Property Protection with Traitor Trackingcited by 9
5 more
- 2023FedRight: An Effective Model Copyright Protection for Federated Learningcited by 8
- 2023Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchaincited by 5
- 2024FedQV: Leveraging Quadratic Voting in Federated Learningcited by 3
- 2023FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Maskscited by 1
- 2023FreqyWM: Frequency Watermarking for the New Data Economycited by 1
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Citing papers per year: 2026: 1.
1 paper.