# FedPoP: Federated Learning Meets Proof of Participation

arXiv: https://arxiv.org/abs/2511.08207 · HTML: https://mlsanity.com/paper/2511.08207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining](https://mlsanity.com/paper/2203.07320/) (2022)
- [WAFFLE: Watermarking in Federated Learning](https://mlsanity.com/paper/2008.07298/) (2020)
- [FedIPR: Ownership Verification for Federated Deep Neural Network Models](https://mlsanity.com/paper/2109.13236/) (2021)
- [FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs](https://mlsanity.com/paper/2306.04959/) (2023)
- [Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring](https://mlsanity.com/paper/2211.07138/) (2022)
- [Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning](https://mlsanity.com/paper/2308.09883/) (2023)
- [FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking](https://mlsanity.com/paper/2306.01356/) (2023)
- [FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof](https://mlsanity.com/paper/2305.04507/) (2023)
- [FedRight: An Effective Model Copyright Protection for Federated Learning](https://mlsanity.com/paper/2303.10399/) (2023)
- [Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain](https://mlsanity.com/paper/2302.14031/) (2023)
- [FedQV: Leveraging Quadratic Voting in Federated Learning](https://mlsanity.com/paper/2401.01168/) (2024)
- [FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks](https://mlsanity.com/paper/2307.14751/) (2023)
- [FreqyWM: Frequency Watermarking for the New Data Economy](https://mlsanity.com/paper/2312.16547/) (2023)

## Built on by (most cited first, 1 of 1)

- [ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoring](https://mlsanity.com/paper/2609.08763/) (2026)

## 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.