Summary of A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning, by Jingbo Zhang and Qiong Wu and Pingyi Fan and Qiang Fan
A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
by Jingbo Zhang, Qiong Wu, Pingyi Fan, Qiang Fan
First submitted to arxiv on: 10 Oct 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 In Federated Edge Learning (FEL), a distributed Machine Learning paradigm, user privacy is ensured by physical separation of each user’s data. However, as IoT and Smart Earth applications grow in complexity, conventional resource allocation schemes struggle to support their increasing demands. To address this scaling problem, joint resource optimization may be the key solution. This paper reviews joint allocation strategies for computation, data, communication, and network topology resources in FEL, highlighting advantages in improving system efficiency, reducing latency, enhancing resource utilization, and boosting robustness. Additionally, it explores the potential of joint optimization to enhance privacy preservation by minimizing communication requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper is about finding ways to make Federated Edge Learning more efficient, reliable, and secure as we deal with increasingly complex applications like the Internet of Things and Smart Earth. By reviewing different approaches to managing resources in FEL, it provides insights into how to make our systems better. |
Keywords
» Artificial intelligence » Boosting » Machine learning » Optimization
