# DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum

arXiv: https://arxiv.org/abs/2306.12608 · HTML: https://mlsanity.com/paper/2306.12608/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [Towards Trustworthy Federated Learning with Untrusted Participants](https://mlsanity.com/paper/2505.01874/) (2025)
- [Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning](https://mlsanity.com/paper/2506.13561/) (2025)
- [FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning](https://mlsanity.com/paper/2511.14715/) (2025)
- [DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models](https://mlsanity.com/paper/2602.19945/) (2026)
- [Byzantine-Robust Optimization under (L0, L1)-Smoothness](https://mlsanity.com/paper/2603.12512/) (2026)
- [Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning](https://mlsanity.com/paper/2606.17035/) (2026)

## Abstract (arXiv)

Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively while keeping their datasets local and only exchanging the gradient or model updates with a coordinating server. Existing FL protocols are vulnerable to attacks that aim to compromise data privacy and/or model robustness. Recently proposed defenses focused on ensuring either privacy or robustness, but not both. In this paper, we focus on simultaneously achieving differential privacy (DP) and Byzantine robustness for cross-silo FL, based on the idea of learning from history. The robustness is achieved via client momentum, which averages the updates of each client over time, thus reducing the variance of the honest clients and exposing the small malicious perturbations of Byzantine clients that are undetectable in a single round but accumulate over time. In our initial solution DP-BREM, DP is achieved by adding noise to the aggregated momentum, and we account for the privacy cost from the momentum, which is different from the conventional DP-SGD that accounts for the privacy cost from the gradient. Since DP-BREM assumes a trusted server (who can obtain clients' local models or updates), we further develop the final solution called DP-BREM+, which achieves the same DP and robustness properties as DP-BREM without a trusted server by utilizing secure aggregation techniques, where DP noise is securely and jointly generated by the clients. Both theoretical analysis and experimental results demonstrate that our proposed protocols achieve better privacy-utility tradeoff and stronger Byzantine robustness than several baseline methods, under different DP budgets and attack settings.