Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning

TL;DR

This paper introduces a semantics-aware client scheduling framework for energy-harvesting federated learning that reduces energy consumption and improves learning efficiency by using a feature-based proxy to estimate update importance.

Contribution

It proposes a lightweight, feature-based proxy for VAoI, enabling practical semantics-aware scheduling in resource-constrained, energy-harvesting federated learning environments.

Findings

Achieves better learning performance under non-IID data and limited energy.

Reduces energy consumption compared to baseline scheduling policies.

Demonstrates practicality of semantics-aware scheduling in real-world scenarios.

Abstract

Federated Learning (FL) on resource-constrained edge devices faces a critical challenge: The computational energy required for training Deep Neural Networks (DNNs) often dominates communication costs. However, most existing Energy-Harvesting FL (EHFL) strategies fail to account for this reality, resulting in wasted energy due to redundant local computations. For efficient and proactive resource management, algorithms that predict local update contributions must be devised. We propose a lightweight client scheduling framework using the Version Age of Information (VAoI), a semantics-aware metric that quantifies update timeliness and significance. Crucially, we overcome VAoI's typical prohibitive computational cost, which requires statistical distance over the entire parameter space, by introducing a feature-based proxy. This proxy estimates model redundancy using intermediate-layer extraction from a single forward pass, dramatically reducing computational complexity. Experiments conducted under extreme non-IID data distributions and scarce energy availability demonstrate superior learning performance while achieving energy reduction compared to existing baseline selection policies. Our framework establishes semantics-aware scheduling as a practical and vital solution for EHFL in realistic scenarios where training costs dominate transmission costs.