# Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises

arXiv: https://arxiv.org/abs/2405.08698 · HTML: https://mlsanity.com/paper/2405.08698/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Byzantine-Resilient Secure Federated Learning](https://mlsanity.com/paper/2007.11115/) (2020)
- [Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy](https://mlsanity.com/paper/1806.00939/) (2018)
- [The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning](https://mlsanity.com/paper/2203.03761/) (2022)
- [Secure Byzantine-Robust Machine Learning](https://mlsanity.com/paper/2006.04747/) (2020)
- [FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.05506/) (2023)
- [Secure Byzantine-Robust Distributed Learning via Clustering](https://mlsanity.com/paper/2110.02940/) (2021)

## Built on by (most cited first, 7 of 7)

- [Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization](https://mlsanity.com/paper/2406.14362/) (2024)
- [LoByITFL: Low Communication Secure and Private Federated Learning](https://mlsanity.com/paper/2405.19217/) (2024)
- [Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning](https://mlsanity.com/paper/2506.13561/) (2025)
- [Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning](https://mlsanity.com/paper/2506.09870/) (2025)
- [Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning](https://mlsanity.com/paper/2508.12978/) (2025)
- [Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries](https://mlsanity.com/paper/2607.20890/) (2026)
- [FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices](https://mlsanity.com/paper/2608.01095/) (2026)

## Abstract (arXiv)

Federated learning (FL) shows great promise in large scale machine learning, but brings new risks in terms of privacy and security. We propose ByITFL, a novel scheme for FL that provides resilience against Byzantine users while keeping the users' data private from the federator and private from other users. The scheme builds on the preexisting non-private FLTrust scheme, which tolerates malicious users through trust scores (TS) that attenuate or amplify the users' gradients. The trust scores are based on the ReLU function, which we approximate by a polynomial. The distributed and privacy-preserving computation in ByITFL is designed using a combination of Lagrange coded computing, verifiable secret sharing and re-randomization steps. ByITFL is the first Byzantine resilient scheme for FL with full information-theoretic privacy.