Summary of Fedrema: Improving Personalized Federated Learning Via Leveraging the Most Relevant Clients, by Han Liang et al.
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by Han Liang, Ziwei Zhan, Weijie Liu, Xiaoxi Zhang, Chee Wei Tan, Xu Chen
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
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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 new algorithm called FedReMa, which is designed to tackle the class-imbalance issue in Personalized Federated Learning (PFL). PFL aims to create personalized models for each client by aggregating their local data distributions. The authors identify two key challenges: identifying and harnessing different clients’ expertise on different data classes, and selecting distinct aggregation methods for clients’ feature extractors and classifiers. To address these challenges, FedReMa employs an adaptive inter-client co-learning approach that assesses and manages task relevance by analyzing the similarities between clients’ logits of their classifiers. The authors demonstrate the superiority of FedReMa in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to do machine learning with lots of devices. Right now, when we do this kind of learning, it can be hard because some devices might have more important information than others. The new way, called FedReMa, helps solve this problem by working together with the devices and using what they know best. It’s like a team effort! The authors tested their idea and found that it works really well. |
Keywords
» Artificial intelligence » Federated learning » Logits » Machine learning
