Summary of Enhancing Group Fairness in Federated Learning Through Personalization, by Yifan Yang et al.
Enhancing Group Fairness in Federated Learning through Personalization
by Yifan Yang, Ali Payani, Parinaz Naghizadeh
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 The paper investigates the impact of personalized Federated Learning (FL) algorithms on the group fairness of learned models. It shows that personalization can lead to improved local fairness as an unintended benefit, and proposes two new fairness-aware federated clustering algorithms: Fair-FCA and Fair-FL+HC. These algorithms extend existing methods like IFCA and FL+HC, and demonstrate their ability to balance accuracy and fairness at the client level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how personalized Federated Learning can make machine learning models fairer. It finds that when each device is given a customized model, it becomes more accurate and also more fair. The paper even shows how to make this fairness benefit better by combining it with other techniques. This research can help us create more honest and transparent AI systems. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Machine learning
