# Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness

arXiv: https://arxiv.org/abs/2404.17970 · HTML: https://mlsanity.com/paper/2404.17970/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 60 references that are in the map.
- First posted on arXiv 27 Apr 2024.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [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)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [Learning from History for Byzantine Robust Optimization](https://mlsanity.com/paper/2012.10333/) (2020)
- [Decentralized Federated Learning: A Segmented Gossip Approach](https://mlsanity.com/paper/1908.07782/) (2019)
- [Zeno++: Robust Fully Asynchronous SGD](https://mlsanity.com/paper/1903.07020/) (2019)
- [BRIDGE: Byzantine-resilient Decentralized Gradient Descent](https://mlsanity.com/paper/1908.08098/) (2019)
- [RelaySum for Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2110.04175/) (2021)
- [ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning](https://mlsanity.com/paper/1708.08155/) (2017)
- [Byzantine-resilient Decentralized Stochastic Gradient Descent](https://mlsanity.com/paper/2002.08569/) (2020)
- [On the (In)security of Peer-to-Peer Decentralized Machine Learning](https://mlsanity.com/paper/2205.08443/) (2022)
- [Secure Aggregation with Heterogeneous Quantization in Federated Learning](https://mlsanity.com/paper/2009.14388/) (2020)
- [Phocas: dimensional Byzantine-resilient stochastic gradient descent](https://mlsanity.com/paper/1805.09682/) (2018)
- [Basil: A Fast and Byzantine-Resilient Approach for Decentralized Training](https://mlsanity.com/paper/2109.07706/) (2021)
- [Can Decentralized Learning be more robust than Federated Learning?](https://mlsanity.com/paper/2303.03829/) (2023)
- [Privacy-preserving Logistic Regression with Secret Sharing](https://mlsanity.com/paper/2105.06869/) (2021)

## Built on by (most cited first, 4 of 4)

- [LoByITFL: Low Communication Secure and Private Federated Learning](https://mlsanity.com/paper/2405.19217/) (2024)
- [A Secure and Private Distributed Bayesian Federated Learning Design](https://mlsanity.com/paper/2602.20003/) (2026)
- [ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning](https://mlsanity.com/paper/2506.15181/) (2025)
- [Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning](https://mlsanity.com/paper/2606.19129/) (2026)

## Abstract (arXiv)

Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting. Yet, it is threatened by the looming threat of Byzantine clients who intentionally disrupt the learning process by broadcasting arbitrary model updates to other clients, seeking to degrade the performance of the global model. In response, robust aggregation schemes have emerged as promising solutions to defend against such Byzantine clients, thereby enhancing the robustness of Decentralized Learning. Defenses against Byzantine adversaries, however, typically require access to the updates of other clients, a counterproductive privacy trade-off that in turn increases the risk of inference attacks on those same model updates. In this paper, we introduce SecureDL, a novel DL protocol designed to enhance the security and privacy of DL against Byzantine threats. SecureDL~facilitates a collaborative defense, while protecting the privacy of clients' model updates through secure multiparty computation. The protocol employs efficient computation of cosine similarity and normalization of updates to robustly detect and exclude model updates detrimental to model convergence. By using MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets, we evaluated SecureDL against various Byzantine attacks and compared its effectiveness with four existing defense mechanisms. Our experiments show that SecureDL is effective even in the case of attacks by the malicious majority (e.g., 80% Byzantine clients) while preserving high training accuracy.