mlsanityData to 13 Oct 2026

Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors

arXiv 2512.03287 ↗First posted 2 Dec 2025cs.LG cs.DCcited by 0

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Abstract (arXiv)

Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized user data, which can pose privacy concerns as they necessitate the uploading of user data to a centralized server. This work proposes multi-frequency Federated Learning (FL) to enable: (1) privacy-aware ML; (2) joint ML model learning across devices with varying sampling frequency. We focus on head-worn devices (e.g., earbuds and smart glasses), a relatively unexplored domain compared to traditional smartwatch- or smartphone-based HAR. Results have shown improvements on two datasets against frequency-specific approaches, indicating a promising future in the multi-frequency FL-HAR task. The proposed network's implementation is publicly available for further research and development.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 2 Dec 2025.

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14 of 40 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2019Advances and Open Problems in Federated Learningcited by 3,040
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2019Towards Federated Learning at Scale: System Designcited by 830
  5. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 502
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 453
  7. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  8. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 251
  9. 2020Federated Learning of a Mixture of Global and Local Modelscited by 209
  10. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  11. 2022Assessing the State of Self-Supervised Human Activity Recognition using Wearablescited by 63
  12. 2022ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognitioncited by 22
2 more
  1. 2021An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligencecited by 11
  2. 2022FLAME: Federated Learning Across Multi-device Environmentscited by 8

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