Summary of Advances in Robust Federated Learning: a Survey with Heterogeneity Considerations, by Chuan Chen et al.
Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations
by Chuan Chen, Tianchi Liao, Xiaojun Deng, Zihou Wu, Sheng Huang, Zibin Zheng
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 tackles a significant challenge in heterogeneous federated learning (FL), where multiple clients with diverse data distributions, model structures, task objectives, computational capabilities, and communication resources collaborate to train models efficiently. To address this complexity, the authors outline the fundamental concepts of heterogeneous FL and identify five key aspects that contribute to heterogeneity: data, model, task, device, and communication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, federated learning is about training AI models across multiple devices without sharing sensitive data. This paper explores how different approaches handle this diversity and categorizes them into three levels: data-level, model-level, and architecture-level. Additionally, the authors discuss privacy-preserving strategies to keep data safe in these collaborative environments. |
Keywords
» Artificial intelligence » Federated learning
