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 $(\mu)$ 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. The code can be found here~{\url{https://github.com/Pranabiitp/FedMRL}}.

翻译：尽管联邦学习（FL）在医学影像诊断领域取得了最新进展，但处理客户端间的数据异质性仍是实际应用中的重大挑战。FL的主要障碍源于各客户端数据样本的非独立同分布特性，这通常导致聚合全局模型的性能下降。本研究提出FedMRL，一种新颖的联邦多智能体深度强化学习框架，旨在解决数据异质性问题。FedMRL引入了一种创新的损失函数以促进客户端间的公平性，防止最终全局模型产生偏差。此外，该框架采用多智能体强化学习（MARL）方法计算个性化局部目标函数的邻近项$(\mu)$，确保收敛至全局最优解。进一步地，FedMRL在服务器端集成基于自组织映射（SOM）的自适应权重调整方法，以抵消客户端本地数据分布间的分布偏移。我们使用两个公开的真实世界医学数据集评估所提方法，结果表明FedMRL显著优于现有先进技术，验证了其在解决联邦学习中数据异质性问题的有效性。代码可见~{\url{https://github.com/Pranabiitp/FedMRL}}。