Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective
Summary
Federated learning (FL) shows promise for privacy in neural interfaces. However, real-time online use reveals performance challenges due to user-decoder co-adaptation, requiring specialized algorithms.
Area of Science:
- Neuroscience
- Machine Learning
- Human-Computer Interaction
Background:
- Neural interfaces enable intuitive interaction but face privacy challenges with sensitive neural data.
- Federated learning (FL) is a privacy-preserving technique, but its application in real-time neural interfaces is underexplored.
Purpose of the Study:
- To propose a framework for applying FL to neural interfaces.
- To evaluate FL-based neural decoding in both offline and real-time settings using surface electromyography.
Main Methods:
- Developed a conceptual framework for FL in neural interfaces.
- Conducted offline simulations and real-time online user studies using high-dimensional surface electromyography (sEMG).
- Assessed performance and privacy trade-offs of FL in neural decoding.
Main Results:
- Offline FL simulations indicated potential for simultaneous performance and privacy enhancement.
- Real-time online experiments showed standard FL assumptions falter with user-decoder co-adaptation.
- FL maintained privacy benefits but introduced performance trade-offs not seen in offline tests.
Conclusions:
- Current FL methods are insufficient for real-time neural decoding due to co-adaptive dynamics.
- Specialized FL algorithms are needed to address the unique challenges of online neural interfaces.
- Further research is required to bridge the gap between offline FL potential and online application realities.
