标题： 开放环和闭环肌电解码中的联邦学习：隐私与性能视角 显示英文标题

标题： Federated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective

摘要： 侵入性和非侵入性神经接口作为下一代技术的高带宽输入设备具有前景。 然而，神经信号本质上编码了关于个体身份和健康状况的敏感信息，使得为解码器训练共享数据成为一项关键的隐私挑战。 联邦学习（FL），一种分布式、保护隐私的学习框架，提供了一个有希望的解决方案，但在闭环自适应神经接口中尚未被探索。 在这里，我们引入基于联邦学习的神经解码，并在开环和闭环场景中使用高维肌电信号系统地评估其性能和隐私。 在开环模拟中，FL显著优于本地学习基线，证明了其在高性能、隐私意识神经解码中的潜力。 相比之下，闭环用户研究需要调整FL方法以适应单用户、实时交互，这是标准FL不支持的场景。 这种修改导致本地学习解码器在闭环性能上超过了调整后的FL方法，但本地学习仍然存在更高的隐私风险。 我们的研究结果突显了实时自适应应用中的关键性能-隐私权衡，并表明需要专门为协同自适应、单用户应用设计的FL方法。 显示英文摘要

摘要： Invasive and non-invasive neural interfaces hold promise as high-bandwidth input devices for next-generation technologies. However, neural signals inherently encode sensitive information about an individual's identity and health, making data sharing for decoder training a critical privacy challenge. Federated learning (FL), a distributed, privacy-preserving learning framework, presents a promising solution, but it remains unexplored in closed-loop adaptive neural interfaces. Here, we introduce FL-based neural decoding and systematically evaluate its performance and privacy using high-dimensional electromyography signals in both open- and closed-loop scenarios. In open-loop simulations, FL significantly outperformed local learning baselines, demonstrating its potential for high-performance, privacy-conscious neural decoding. In contrast, closed-loop user studies required adapting FL methods to accommodate single-user, real-time interactions, a scenario not supported by standard FL. This modification resulted in local learning decoders surpassing the adapted FL approach in closed-loop performance, yet local learning still carried higher privacy risks. Our findings highlight a critical performance-privacy tradeoff in real-time adaptive applications and indicate the need for FL methods specifically designed for co-adaptive, single-user applications.