Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation

TL;DR

This paper introduces FedBacys, a cyclic scheduling framework for energy-harvesting federated learning that reduces energy consumption and enhances stability by selectively involving clients based on battery levels.

Contribution

It proposes a novel battery-aware cyclic scheduling method with a variant that enables selective participation, improving energy efficiency in energy-harvesting federated learning systems.

Findings

FedBacys reduces energy consumption compared to existing methods.

FedBacys-Odd further decreases energy costs with minimal performance loss.

The framework demonstrates improved robustness and convergence in experiments.

Abstract

Federated Learning (FL) is a powerful paradigm for distributed learning, but its increasing complexity leads to significant energy consumption from client-side computations for training models. In particular, the challenge is critical in energy-harvesting FL (EHFL) systems where participation availability of each device oscillates due to limited energy. To address this, we propose FedBacys, a battery-aware EHFL framework using cyclic client participation based on users' battery levels. By clustering clients and scheduling them sequentially, FedBacys minimizes redundant computations, reduces system-wide energy usage, and improves learning stability. We also introduce FedBacys-Odd, a more energy-efficient variant that allows clients to participate selectively, further reducing energy costs without compromising performance. We provide a convergence analysis for our framework and demonstrate its superior energy efficiency and robustness compared to existing algorithms through numerical experiments.