# An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging

arXiv: https://arxiv.org/abs/2006.07218 · HTML: https://mlsanity.com/paper/2006.07218/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 20 of 20)

- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [The Privacy Power of Correlated Noise in Decentralized Learning](https://mlsanity.com/paper/2405.01031/) (2024)
- [Towards Trustworthy Federated Learning with Untrusted Participants](https://mlsanity.com/paper/2505.01874/) (2025)
- [Differentially private and decentralized randomized power method](https://mlsanity.com/paper/2411.01931/) (2024)
- [Low-Cost Privacy-Preserving Decentralized Learning](https://mlsanity.com/paper/2403.11795/) (2024)
- [Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via f-Differential Privacy](https://mlsanity.com/paper/2510.19934/) (2025)
- [Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization](https://mlsanity.com/paper/2306.16077/) (2023)
- [Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models](https://mlsanity.com/paper/2505.19969/) (2025)
- [Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning](https://mlsanity.com/paper/2506.18020/) (2025)
- [Unified Privacy Guarantees for Decentralized Learning via Matrix Factorization](https://mlsanity.com/paper/2510.17480/) (2025)
- [Balancing Privacy, Robustness, and Efficiency in Machine Learning](https://mlsanity.com/paper/2312.14712/) (2023)
- [On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps](https://mlsanity.com/paper/2407.19286/) (2024)
- [Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixing](https://mlsanity.com/paper/2412.12837/) (2024)
- [How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations](https://mlsanity.com/paper/2507.01487/) (2025)
- [SoK: Verifiable Cross-Silo FL](https://mlsanity.com/paper/2410.09124/) (2024)
- [Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation](https://mlsanity.com/paper/2506.03746/) (2025)
- [On Balancing Sparsity with Reliable Connectivity in Distributed Network Design with Random K-out Graphs](https://mlsanity.com/paper/2508.11863/) (2025)
- [DP-Hype: Federated Differentially Private Hyperparameter Search](https://mlsanity.com/paper/2510.04902/) (2025)
- [Accurate, private, secure, federated U-statistics with higher degree](https://mlsanity.com/paper/2603.01986/) (2026)
- [End-to-End Verifiable and Robust Federated Learning](https://mlsanity.com/paper/2609.15521/) (2026)

## Abstract (arXiv)

Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges in the context of distributed averaging, an essential building block of federated learning algorithms. Our first contribution is a scalable protocol in which participants exchange correlated Gaussian noise along the edges of a network graph, complemented by independent noise added by each party. We analyze the differential privacy guarantees of our protocol and the impact of the graph topology under colluding malicious parties, showing that we can nearly match the utility of the trusted curator model even when each honest party communicates with only a logarithmic number of other parties chosen at random. This is in contrast with protocols in the local model of privacy (with lower utility) or based on secure aggregation (where all pairs of users need to exchange messages). Our second contribution enables users to prove the correctness of their computations without compromising the efficiency and privacy guarantees of the protocol. Our verification protocol relies on standard cryptographic primitives like commitment schemes and zero knowledge proofs.