# Secure Byzantine-Robust Machine Learning

arXiv: https://arxiv.org/abs/2006.04747 · HTML: https://mlsanity.com/paper/2006.04747/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [Byzantine-Robust Decentralized Learning via ClippedGossip](https://mlsanity.com/paper/2202.01545/) (2022)
- [Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees](https://mlsanity.com/paper/2205.11765/) (2022)
- [Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?](https://mlsanity.com/paper/2102.08166/) (2021)
- [Secure Byzantine-Robust Distributed Learning via Clustering](https://mlsanity.com/paper/2110.02940/) (2021)
- [Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises](https://mlsanity.com/paper/2405.08698/) (2024)
- [Privacy-Preserving Federated Unlearning with Certified Client Removal](https://mlsanity.com/paper/2404.09724/) (2024)
- [Combining Differential Privacy and Byzantine Resilience in Distributed SGD](https://mlsanity.com/paper/2110.03991/) (2021)
- [Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning](https://mlsanity.com/paper/2209.07716/) (2022)
- [PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy](https://mlsanity.com/paper/2110.11578/) (2021)
- [LoByITFL: Low Communication Secure and Private Federated Learning](https://mlsanity.com/paper/2405.19217/) (2024)
- [Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning](https://mlsanity.com/paper/2512.17254/) (2025)
- [DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation](https://mlsanity.com/paper/2509.08449/) (2025)
- [Balancing Privacy, Robustness, and Efficiency in Machine Learning](https://mlsanity.com/paper/2312.14712/) (2023)
- [Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning](https://mlsanity.com/paper/2506.13561/) (2025)
- [SABLE: Secure And Byzantine robust LEarning](https://mlsanity.com/paper/2309.05395/) (2023)
- [Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning](https://mlsanity.com/paper/2401.05562/) (2024)

## Abstract (arXiv)

Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentralized training raises a number of concerns about the robustness, privacy, and security of the procedure. While extensive work has been done in tackling with robustness, privacy, or security individually, their combination has rarely been studied. In this paper, we propose a secure two-server protocol that offers both input privacy and Byzantine-robustness. In addition, this protocol is communication-efficient, fault-tolerant and enjoys local differential privacy.