# Can Decentralized Learning be more robust than Federated Learning?

arXiv: https://arxiv.org/abs/2303.03829 · HTML: https://mlsanity.com/paper/2303.03829/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [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)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [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)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [Learning from History for Byzantine Robust Optimization](https://mlsanity.com/paper/2012.10333/) (2020)
- [Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning](https://mlsanity.com/paper/2108.10241/) (2021)
- [Eluding Secure Aggregation in Federated Learning via Model Inconsistency](https://mlsanity.com/paper/2111.07380/) (2021)
- [Data Poisoning Attacks on Federated Machine Learning](https://mlsanity.com/paper/2004.10020/) (2020)
- [Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)](https://mlsanity.com/paper/2008.00742/) (2020)
- [BRIDGE: Byzantine-resilient Decentralized Gradient Descent](https://mlsanity.com/paper/1908.08098/) (2019)
- [Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning](https://mlsanity.com/paper/1806.05358/) (2018)
- [Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning](https://mlsanity.com/paper/2208.09894/) (2022)
- [Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey](https://mlsanity.com/paper/2112.10183/) (2021)
- [Security Analysis of SplitFed Learning](https://mlsanity.com/paper/2212.01716/) (2022)
- [Network-Level Adversaries in Federated Learning](https://mlsanity.com/paper/2208.12911/) (2022)

## Built on by (most cited first, 6 of 6)

- [Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness](https://mlsanity.com/paper/2404.17970/) (2024)
- [The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks](https://mlsanity.com/paper/2311.07946/) (2023)
- [Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning](https://mlsanity.com/paper/2404.06230/) (2024)
- [On Gossip Algorithms for Machine Learning with Pairwise Objectives](https://mlsanity.com/paper/2603.24128/) (2026)
- [Optimizing Byzantine Node Placement in Decentralized Federated Learning](https://mlsanity.com/paper/2609.01495/) (2026)
- [Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration](https://mlsanity.com/paper/2609.07312/) (2026)

## Abstract (arXiv)

Decentralized Learning (DL) is a peer--to--peer learning approach that allows a group of users to jointly train a machine learning model. To ensure correctness, DL should be robust, i.e., Byzantine users must not be able to tamper with the result of the collaboration. In this paper, we introduce two textitnew attacks against DL where a Byzantine user can: make the network converge to an arbitrary model of their choice, and exclude an arbitrary user from the learning process. We demonstrate our attacks' efficiency against Self--Centered Clipping, the state--of--the--art robust DL protocol. Finally, we show that the capabilities decentralization grants to Byzantine users result in decentralized learning always providing less robustness than federated learning.