mlsanityData to 14 Oct 2026

Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective

arXiv 2507.12652 ↗First posted 16 Jul 2025cs.LG cs.CR cs.HCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 110 references that are in the map. First posted on arXiv 16 Jul 2025.

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32 of 110 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2019Deep Leakage from Gradientscited by 1,021
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  6. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  8. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  9. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  10. 2020Adaptive Personalized Federated Learningcited by 358
  11. 2019Asynchronous Federated Optimizationcited by 267
  12. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
20 more
  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  3. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  4. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  5. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  6. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  7. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  8. 2023PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmarkcited by 17
  9. 2023Securing Distributed SGD against Gradient Leakage Threatscited by 14
  10. 2022Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropoutcited by 12
  11. 2022Federated Data Analytics: A Study on Linear Modelscited by 9
  12. 2024Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfacescited by 7
  13. 2023Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learningcited by 6
  14. 2022PersA-FL: Personalized Asynchronous Federated Learningcited by 5
  15. 2023Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classificationcited by 5
  16. 2021Privacy-Protecting Techniques for Behavioral Biometric Data: A Surveycited by 3
  17. 2016Cortical Brain Computer Interface for Closed-Loop Deep Brain Stimulationcited by 2
  18. 2017A Window into the Soul: Biosensing in Publiccited by 1
  19. 2022"I'm Just Overwhelmed": Investigating Physical Therapy Accessibility and Technology Interventions for People with Disabilities and/or Chronic Conditionscited by 1
  20. 2024Vision-Based Hand Gesture Customization from a Single Demonstrationcited by 1

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