FedMRL: 面向医学影像的感知数据异构性的联邦多智能体深度强化学习
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging
- Indian Institute of Technology Patna（印度理工学院巴特那分校）
- Rajiv Gandhi Institute of Petroleum Technology（拉吉夫·甘地石油技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对联邦医学影像学习中客户端数据非独立同分布导致的全局模型性能下降问题，提出融合公平损失、MARL近端项计算与SOM自适应权重调整的FedMRL框架，在两个公开医学数据集上性能优于现有最优方法。
AI中文摘要：
尽管用于医学影像诊断的联邦学习（FL）近期取得了进展，但解决客户端间的数据异构性问题仍是实际落地的重大挑战。联邦学习的主要障碍源于各客户端数据样本的非独立同分布（non-IID）特性，这通常会导致聚合后的全局模型性能下降。本研究提出FedMRL，一种旨在解决数据异构性的新型联邦多智能体深度强化学习框架。FedMRL引入了一种新型损失函数以促进客户端间的公平性，避免最终全局模型出现偏差。此外，它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近端项$(μ)$，确保收敛至全局最优。再者，FedMRL在服务器端集成了基于自组织映射（SOM）的自适应权重调整方法，以抵消客户端局部数据分布间的偏移。我们使用两个公开的真实医学数据集评估了所提方法，结果表明FedMRL显著优于现有最优技术，证明了其在解决联邦学习中数据异构性问题上的有效性。代码可在以下链接获取：https://github.com/Pranabiitp/FedMRL
英文摘要：
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. The code can be found here~{\url{https://github.com/Pranabiitp/FedMRL}}.
