提出几何感知的联邦学习框架，提升脑电信号处理精度与通信效率。
FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
- 采用投影平均与流形逼近实现正交约束下的参数聚合
- 在脑机接口数据上F1得分更高，且抗部分参与能力更强
- 适用于低带宽、分布式信号处理场景
我们为基于对称正定（SPD）矩阵与施蒂费尔流形约束参数的经典SPDnet模型，提出了两种联邦学习框架。不同于破坏正交性的标准欧氏平均，我们的方法通过两种高效聚合策略保持几何结构：ProjAvg将算术均值投影到施蒂费尔流形上，RLAvg通过再映射和提升近似切空间平均。两种方法计算高效、与优化器无关，支持信号处理中特征为SPD矩阵的可扩展联邦学习。在脑电运动想象基准测试中，FedSPDnet相比联邦EEGnet在F1得分和对联邦化及部分参与的鲁棒性方面表现更优，且每轮通信参数更少。
原文摘要 · Abstract (English)
We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. Unlike standard Euclidean averaging, which violates orthogonality, our approach preserves geometric structure through two efficient aggregation strategies: ProjAvg, projecting arithmetic means onto the Stiefel manifold, and RLAvg, approximating tangent-space averaging via retractions and liftings. Both methods are computationally efficient, independent of the optimizer, and enable scalable federated learning for signal processing applications whose features are SPD matrices. Simulations on EEG motor imagery benchmarks show that FedSPDnet outperforms federated EEGnet in F1 score and robustness to federation and partial participation, while using fewer parameters per communication round.
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