Joint optimization improves edge sensing computation and communication
Learning OTA: A Unified Framework for Edge Sensing, Computation, and Communication
Information TheoryNetworking and Internet Architecture
Summary
Getting good data from many different sensors on devices like phones or cameras is tricky because the sensors, computing power, and communication all affect each other. The authors looked at how these factors work together when devices send updates to a central server using a wireless method that mixes signals in the air. They made a model that captures how noise, energy use, and delay impact the learning process. Their work helps decide the best way for devices to set sensor quality, power, and data amount to speed up learning while saving energy. Experiments show their approach can improve learning by balancing sensing, computing, and communicating well.
What this means in practice
- •For edge device engineers: Optimize sensor settings, power, and communication in distributed learning devices to improve model accuracy and reduce energy use.
- •For wireless network designers: Design wireless aggregation protocols that jointly consider sensing quality and communication distortion for better learning performance.
Authors
Mehdi Karbalayghareh, David J. Love, Christopher G. Brinton
Abstract
Most federated learning (FL) studies implicitly assume that training data is readily available at participating edge devices. In practice, this data must first be acquired through heterogeneous sensors with potentially different measurement modalities, and the quality and cost of this acquisition process can directly affect the learning performance. This dependence creates a coupled relationship among sensing, computation, and communication, necessitating a system model that captures their joint effect on resource efficiency and learning accuracy. This paper develops such a unified treatment for an over-the-air (OTA) FL system in which each device configures its sensing modality, power, resolution, and sample size, with aggregations conducted OTA at the server. We derive models for sensing noise, energy consumption, latency, and OTA aggregation distortion, and establish a non-convex convergence bound quantifying how these factors influence the global model trajectory. Guided by this analysis, we formulate a per-round optimization that balances energy, latency, and learning quality under physical device constraints. The resulting mixed optimization is addressed through an alternating procedure with closed-form and one-dimensional updates for continuous variables and a lightweight discrete search. Numerical experiments corroborate the advantage of jointly optimizing sensing, computation, and communication decisions.