Summary of Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics For Federated Learning, by Yanli Li et al.
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
by Yanli Li, Jehad Ibrahim, Huaming Chen, Dong Yuan, Kim-Kwang Raymond Choo
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 the challenge of evaluating federated learning (FL) algorithms comprehensively, given their diverse applications and requirements. The authors introduce the Holistic Evaluation Metrics (HEM) framework to address this gap. HEM incorporates various aspects such as accuracy, convergence, computational efficiency, fairness, and personalization, with importance vectors assigned for each use case. Experimental results demonstrate that HEM can effectively identify the best-suited FL algorithm for specific scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning algorithms are used in many different ways, but it’s hard to decide which one is best for a particular job. To fix this problem, researchers created something called Holistic Evaluation Metrics (HEM). This helps us compare and choose the right algorithm for each situation. They tested HEM on three types of applications: Internet of Things, smart devices, and institutions. The results show that HEM works well and can help find the best algorithm for a specific task. |
Keywords
» Artificial intelligence » Federated learning
