# Secure Byzantine-Robust Distributed Learning via Clustering

arXiv: https://arxiv.org/abs/2110.02940 · HTML: https://mlsanity.com/paper/2110.02940/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [Robust Federated Learning in a Heterogeneous Environment](https://mlsanity.com/paper/1906.06629/) (2019)
- [Zeno++: Robust Fully Asynchronous SGD](https://mlsanity.com/paper/1903.07020/) (2019)
- [Secure Byzantine-Robust Machine Learning](https://mlsanity.com/paper/2006.04747/) (2020)
- [Towards Bidirectional Protection in Federated Learning](https://mlsanity.com/paper/2010.01175/) (2020)

## Built on by (most cited first, 12 of 12)

- [Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees](https://mlsanity.com/paper/2205.11765/) (2022)
- [Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises](https://mlsanity.com/paper/2405.08698/) (2024)
- [zPROBE: Zero Peek Robustness Checks for Federated Learning](https://mlsanity.com/paper/2206.12100/) (2022)
- [ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment](https://mlsanity.com/paper/2302.09913/) (2023)
- [FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning](https://mlsanity.com/paper/2303.05206/) (2023)
- [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)
- [Inferring Communities of Interest in Collaborative Learning-based Recommender Systems](https://mlsanity.com/paper/2306.08929/) (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)
- [Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning](https://mlsanity.com/paper/2606.19129/) (2026)

## Abstract (arXiv)

Federated learning systems that jointly preserve Byzantine robustness and privacy have remained an open problem. Robust aggregation, the standard defense for Byzantine attacks, generally requires server access to individual updates or nonlinear computation -- thus is incompatible with privacy-preserving methods such as secure aggregation via multiparty computation. To this end, we propose SHARE (Secure Hierarchical Robust Aggregation), a distributed learning framework designed to cryptographically preserve client update privacy and robustness to Byzantine adversaries simultaneously. The key idea is to incorporate secure averaging among randomly clustered clients before filtering malicious updates through robust aggregation. Experiments show that SHARE has similar robustness guarantees as existing techniques while enhancing privacy.