This repository includes the source code for MICCAI 2024 paper entitled: "FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging".

Despite recent advancements in federated learning (FL) for medical image diagnosis, addressing data heterogeneity among clients remains a significant challenge for practical implementation. A primary hurdle in FL arises from the non-IID nature of data samples across clients, which typically results in a decline in the performance of the aggregated global model. In this study, we introduce FedMRL, a novel federated multi-agent deep reinforcement learning framework designed to address data heterogeneity. FedMRL incorporates a novel loss function to facilitate fairness among clients, preventing bias in the final global model. Additionally, it employs a multi-agent reinforcement learning (MARL) approach to calculate the proximal term (μ) for the personalized local objective function, ensuring convergence to the global optimum. Furthermore, FedMRL integrates an adaptive weight adjustment method using a Self-organizing map (SOM) on the server side to counteract distribution shifts among clients’ local data distributions. We assess our approach using two publicly available real-world medical datasets, and the results demonstrate that FedMRL significantly outperforms state-of-the-art techniques, showing its efficacy in addressing data heterogeneity in federated learning.

• Tensorflow = 2.10.0
• scikit-learn = 1.3.2

Please download the ISIC 2018 using this link and Messidor dataset using this link.

To divide the dataset into the aprequired no. of clients, divide the data into training, validation and testing set as mentioned in the paper, then run non_iid_data_preparation.py and choose the required dataset (ISIC 2018 and Messidor) and then change the degree of heterogenity (eta) as required. you will get the desired distribution for each client.

To choose the pretrained model, run model.py.

After done with above process, you can run the FedMRL, our proposed method.

After federated training, run Evaluation.py to acess the evaluation metrics such as accuracy, precision, recall etc.

@misc{sahoo2024fedmrldataheterogeneityaware,
title={FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging},
author={Pranab Sahoo and Ashutosh Tripathi and Sriparna Saha and Samrat Mondal},
year={2024},
eprint={2407.05800},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.05800},
}