Summary of Mh-pflid: Model Heterogeneous Personalized Federated Learning Via Injection and Distillation For Medical Data Analysis, by Luyuan Xie et al.
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
by Luyuan Xie, Manqing Lin, Tianyu Luan, Cong Li, Yuejian Fang, Qingni Shen, Zhonghai Wu
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 A novel federated learning paradigm, MH-pFLID, is introduced to overcome challenges in aggregating non-IID data across heterogeneous clients. Current methods require public datasets, posing privacy concerns, while also demanding local computing resources. The proposed framework uses a lightweight messenger model to collect information from each client, and receiver-transmitter modules for efficient injection and distillation of knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is used in medicine to train global models without needing local data access. But there are challenges when clients have different computers and networks. Current methods need public datasets, which can be a problem because they might not be private or might take up too much space on medical devices. Our new method, MH-pFLID, solves these problems by using a lightweight message to collect information from each client. |
Keywords
» Artificial intelligence » Distillation » Federated learning
