# Byzantine-Resilient Federated Learning via Distributed Optimization

arXiv: https://arxiv.org/abs/2503.10792 · HTML: https://mlsanity.com/paper/2503.10792/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (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)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [See through Gradients: Image Batch Recovery via GradInversion](https://mlsanity.com/paper/2104.07586/) (2021)
- [Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing](https://mlsanity.com/paper/2006.09365/) (2020)
- [Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search](https://mlsanity.com/paper/1903.12269/) (2019)
- [Byzantine-Resilient Secure Federated Learning](https://mlsanity.com/paper/2007.11115/) (2020)
- [On the (In)security of Peer-to-Peer Decentralized Machine Learning](https://mlsanity.com/paper/2205.08443/) (2022)
- [Defending against the Label-flipping Attack in Federated Learning](https://mlsanity.com/paper/2207.01982/) (2022)
- [Distributed Optimization Using the Primal-Dual Method of Multipliers](https://mlsanity.com/paper/1702.00841/) (2017)
- [Convergence Rates with Inexact Non-expansive Operators](https://mlsanity.com/paper/1404.4837/) (2014)
- [Derivation and Analysis of the Primal-Dual Method of Multipliers Based on Monotone Operator Theory](https://mlsanity.com/paper/1706.02654/) (2017)
- [Distributed Optimisation with Linear Equality and Inequality Constraints using PDMM](https://mlsanity.com/paper/2309.12897/) (2023)
- [From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges](https://mlsanity.com/paper/2503.07505/) (2025)
- [Revisiting the Primal-Dual Method of Multipliers for Optimisation over Centralised Networks](https://mlsanity.com/paper/2107.08809/) (2021)
- [Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem](https://mlsanity.com/paper/2409.14260/) (2024)
- [Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical Study](https://mlsanity.com/paper/2409.14261/) (2024)
- [Topology-Dependent Privacy Bound For Decentralized Federated Learning](https://mlsanity.com/paper/2312.07956/) (2023)
- [Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss](https://mlsanity.com/paper/2409.10226/) (2024)

## Built on by (most cited first, 3 of 3)

- [Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness](https://mlsanity.com/paper/2510.27213/) (2025)
- [A Byzantine-Robust Federated Learning with Nesterov Momentum](https://mlsanity.com/paper/2511.02657/) (2025)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)

## Abstract (arXiv)

Byzantine attacks present a critical challenge to Federated Learning (FL), where malicious participants can disrupt the training process, degrade model accuracy, and compromise system reliability. Traditional FL frameworks typically rely on aggregation-based protocols for model updates, leaving them vulnerable to sophisticated adversarial strategies. In this paper, we demonstrate that distributed optimization offers a principled and robust alternative to aggregation-centric methods. Specifically, we show that the Primal-Dual Method of Multipliers (PDMM) inherently mitigates Byzantine impacts by leveraging its fault-tolerant consensus mechanism. Through extensive experiments on three datasets (MNIST, FashionMNIST, and Olivetti), under various attack scenarios including bit-flipping and Gaussian noise injection, we validate the superior resilience of distributed optimization protocols. Compared to traditional aggregation-centric approaches, PDMM achieves higher model utility, faster convergence, and improved stability. Our results highlight the effectiveness of distributed optimization in defending against Byzantine threats, paving the way for more secure and resilient federated learning systems.