Summary of Fedpae: Peer-adaptive Ensemble Learning For Asynchronous and Model-heterogeneous Federated Learning, by Brianna Mueller et al.
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning
by Brianna Mueller, W. Nick Street, Stephen Baek, Qihang Lin, Jingyi Yang, Yankun Huang
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized personalized federated learning algorithm that supports model heterogeneity and asynchronous learning. Existing FL paradigms face challenges due to heterogeneity in client data distributions and system capabilities, but FedPAE addresses these limitations by utilizing a peer-to-peer model sharing mechanism and ensemble selection to achieve a refined balance between local and global information. The approach is evaluated against existing state-of-the-art pFL algorithms, demonstrating robustness against statistical heterogeneity and outperforming other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedPAE is a new way for many devices with different types of data to work together and share their knowledge without sharing their individual data. This helps keep sensitive information private while still improving the overall learning process. The method uses peer-to-peer sharing and selects the best models from each device to achieve better results. |
Keywords
» Artificial intelligence » Federated learning
