Summary of Personalized Federated Learning on Heterogeneous and Long-tailed Data Via Expert Collaborative Learning, by Fengling Lv and Xinyi Shang and Yang Zhou and Yiqun Zhang and Mengke Li and Yang Lu
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
by Fengling Lv, Xinyi Shang, Yang Zhou, Yiqun Zhang, Mengke Li, Yang Lu
First submitted to arxiv on: 4 Aug 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 proposes Expert Collaborative Learning (ECL), a method to address the challenges of global long-tailed distribution and data heterogeneity in Personalized Federated Learning (PFL). PFL aims to develop customized models for each client without sharing raw data. However, real-world datasets often follow long-tailed distributions, with more common general health notes compared to those related to specific diseases. This can significantly degrade PFL model performance. Additionally, diverse environments lead to data heterogeneity, which is a classic challenge in federated learning. The authors’ ECL method involves multiple experts, each training on different subsets of data, ensuring minority classes receive sufficient training. Synergistic collaboration produces the final prediction output. Vanilla ECL outperforms state-of-the-art PFL methods on several benchmark datasets with varying degrees of data heterogeneity and long-tailed distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers learn together without sharing all their data. Right now, computers can’t learn from each other’s unique information because it’s too sensitive. But what if we could combine their knowledge in a special way? This would help them become better at recognizing patterns and making predictions. The problem is that some data might be more common than others, which makes it hard for the computers to learn together. Also, different computers might have very different types of information, which can cause problems too. The authors created a new method called Expert Collaborative Learning (ECL) that solves these problems. They showed that this method works better than other ways of making computers learn together on several important datasets. |
Keywords
» Artificial intelligence » Federated learning
