Multi-pulse magnetic resonance imaging (MRI) is widely utilized for clinical practice such as Alzheimer's disease diagnosis. To train a robust model for multi-pulse MRI classification, it requires large and diverse data from various medical institutions while protecting privacy by preventing raw data sharing across institutions. Although federated learning (FL) is a feasible solution to address this issue, it poses challenges of model convergence due to the effect of data heterogeneity and substantial communication overhead due to large numbers of parameters transmitted within the model. To address these challenges, we propose CEPerFed, a communication-efficient personalized FL method. It mitigates the effect of data heterogeneity by incorporating client-side historical risk gradients and historical mean gradients to coordinate local and global optimization. The former is used to weight the contributions from other clients, enhancing the reliability of local updates, while the latter enforces consistency between local updates and the global optimization direction to ensure stable convergence across heterogeneous data distributions. To address the high communication overhead, we propose a hierarchical SVD (HSVD) strategy that transmits only the most critical information required for model updates. Experiments on five classification tasks demonstrate the effectiveness of the CEPerFed method. The code will be released upon acceptance at https://github.com/LD0416/CEPerFed.

翻译：多脉冲磁共振成像（MRI）在阿尔茨海默病诊断等临床实践中得到广泛应用。为训练一个稳健的多脉冲MRI分类模型，需要来自不同医疗机构的大量多样化数据，同时需通过禁止跨机构原始数据共享来保护隐私。尽管联邦学习（FL）是解决此问题的可行方案，但由于数据异质性的影响，其存在模型收敛的挑战；同时因模型内传输大量参数而产生显著的通信开销。为应对这些挑战，我们提出了CEPerFed，一种通信高效的个性化联邦学习方法。该方法通过引入客户端侧的历史风险梯度和历史平均梯度来协调局部与全局优化，从而缓解数据异质性的影响。前者用于加权其他客户端的贡献，增强局部更新的可靠性；后者则强制局部更新与全局优化方向保持一致，以确保在异构数据分布下实现稳定收敛。针对高通信开销问题，我们提出了一种分层奇异值分解（HSVD）策略，仅传输模型更新所需的最关键信息。在五个分类任务上的实验验证了CEPerFed方法的有效性。代码将在论文录用后发布于 https://github.com/LD0416/CEPerFed。