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[2507.12652] Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective
Summary
This article explores the application of federated learning (FL) in offline and online EMG decoding, addressing privacy and performance challenges in neural interfaces.
Why It Matters
As neural interfaces become more prevalent, ensuring the privacy of sensitive data while maintaining performance is crucial. This study highlights the limitations of current FL methodologies in real-time applications, emphasizing the need for tailored algorithms to enhance user experience and data security.
Key Takeaways
- Federated learning can enhance privacy in neural interface applications.
- Offline simulations show performance improvements, but online results reveal complexities.
- Real-time interactions introduce challenges not accounted for in offline studies.
- Current FL assumptions may not apply to sequential user interactions.
- Specialized algorithms are needed to address the unique dynamics of neural decoding.
Computer Science > Machine Learning arXiv:2507.12652 (cs) [Submitted on 16 Jul 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective Authors:Kai Malcolm, César Uribe, Momona Yamagami View a PDF of the paper titled Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective, by Kai Malcolm and 2 other authors View PDF HTML (experimental) Abstract: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 ten...
