Summary of Byzantine-robust Aggregation For Securing Decentralized Federated Learning, by Diego Cajaraville-aboy et al.
Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
by Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo, Manuel Fernández-Veiga
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel algorithm for Decentralized Federated Learning (DFL) to enhance security by handling Byzantine attacks. The algorithm, called WFAgg, uses multiple filters to identify and mitigate these attacks in dynamic decentralized topologies. The proposed method outperforms state-of-the-art centralized Byzantine-robust aggregation schemes on an IID image classification problem in both centralized and decentralized scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a type of artificial intelligence (AI) called Decentralized Federated Learning more secure. Right now, people are worried that AI models being trained on devices could be hacked or manipulated by bad actors. The authors of this paper came up with a new way to make sure these models are robust against such attacks. They tested their method and found it works better than existing methods. |
Keywords
» Artificial intelligence » Federated learning » Image classification
