用联邦学习保护肌电数据隐私，发现线上使用时性能会下降。
Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective
- 将联邦学习应用于肌电解码，设计适应神经接口的框架。
- 离线实验提升性能和隐私，但在线实验性能下降。
- 适合关注神经接口隐私与实时性平衡的研究者。
神经接口为高带宽、直观的人机交互提供可能，但神经数据的高度敏感性给大规模模型训练带来重大隐私挑战。联邦学习（FL）被视为一种有前景的隐私保护方案，但其在实时神经接口中的有效性尚未被探索。本研究提出一个概念框架，将联邦学习应用于神经接口的独特约束，并通过离线仿真和真实实时用户实验，系统评估基于联邦学习的高维肌电（EMG）解码效果。离线结果表明，联邦学习可同时提升性能与隐私；然而在线实验揭示更复杂的情况：标准联邦学习假设难以适用于人机协同适应的实时连续交互。结果显示，尽管联邦学习仍具隐私优势，但引入了离线模拟未预测到的性能权衡。这些发现指出现有联邦学习方法的关键缺口，强调需开发专门算法以应对序列化用户-解码器协同动态。
原文摘要 · Abstract (English)
Neural interfaces offer a pathway to intuitive, high-bandwidth interaction, but the sensitive nature of neural data creates significant privacy hurdles for large-scale model training. Federated learning (FL) has emerged as a promising privacy-preserving solution, yet its efficacy in real-time, online neural interfaces remains unexplored. In this study, we 1) propose a conceptual framework for applying FL to the distinct constraints of neural interface application and 2) provide a systematic evaluation of FL-based neural decoding using high-dimensional electromyography (EMG) across both offline simulations and a real-time, online user study. While offline results suggest that FL can simultaneously enhance performance and privacy, our online experiments reveal a more complex landscape. We found that standard FL assumptions struggle to translate to real-time, sequential interactions with human-decoder co-adaptation. Our results show that while FL retains privacy advantages, it introduces performance tensions not predicted by offline simulations. These findings identify a critical gap in current FL methodologies and highlight the need for specialized algorithms designed to navigate the unique co-adaptive dynamics of sequential-user neural decoding.
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