Summary of Fedmrl: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning For Medical Imaging, by Pranab Sahoo et al.
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging
by Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha, Samrat Mondal
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 study addresses the challenge of data heterogeneity in federated learning (FL) for medical image diagnosis. A novel framework, FedMRL, is introduced to tackle this issue by incorporating a fairness-promoting loss function and a multi-agent reinforcement learning approach. The framework also employs an adaptive weight adjustment method using Self-organizing maps on the server side to counteract distribution shifts among clients’ local data distributions. FedMRL outperforms state-of-the-art techniques on two publicly available medical datasets, demonstrating its efficacy in addressing data heterogeneity in FL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning (FL) helps doctors diagnose diseases using images from different hospitals. But there’s a problem: each hospital has different kinds of images, which makes it hard to combine them into one good model. A team of researchers created a new way to do this called FedMRL. It’s like a game where computers work together to find the best solution. This approach helps ensure that all hospitals’ images are treated fairly and equally, which makes the final diagnosis better. The study tested FedMRL on real-world medical datasets and showed it outperforms existing methods. |
Keywords
» Artificial intelligence » Federated learning » Loss function » Reinforcement learning
