Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette
arXiv:2605.24062v1 Announce Type: new Abstract: Human-body communication (HBC) is a promising physical substrate for wearable body-area networks because it can localize communication around the body and reduce the burden of conventional radio links. Federated learning (FL) is a promising learning substrate because it can reduce raw-data centralization for physiological and behavioral sensing. Yet these two literatures remain weakly connected: FL for wearables usually abstracts the communication layer, whereas HBC research usually abstracts learning and model-update traffic. This article survey
The proliferation of wearable technologies and the increasing demand for on-device intelligence necessitate novel communication and learning paradigms that address privacy and computational constraints.
A strategic reader should care because this research addresses the fundamental challenges of privacy-preserving machine learning in the rapidly growing edge computing and wearable technology sectors, enabling more robust and secure applications.
The explicit connection between federated learning and human-body communication creates a more integrated and efficient architecture for pervasive on-body intelligence, potentially standardizing how wearables process and share data.
- · Wearable technology manufacturers
- · Healthcare providers (remote monitoring)
- · Edge AI developers
- · Personal data privacy solution providers
- · Centralized cloud data processing models (for sensitive on-body data)
- · Traditional wireless communication protocols (for on-body networks)
- · Companies with weak data privacy practices
On-body devices will become more autonomous and intelligent, processing sensitive data closer to the source without constant cloud reliance.
This technical integration could accelerate the development of advanced personalized health monitoring and intervention systems, leveraging real-time, privacy-preserving insights.
The widespread adoption of such secure, on-body AI could set new global standards for personal data handling, influencing regulatory frameworks and consumer expectations for privacy and autonomy.
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