根据电池电量动态调度客户端，提升能量采集联邦学习能效与稳定性
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning
- 按电池水平分组并循环安排客户端参与训练
- 能耗降低37%，非独立同分布数据下仍保持稳定性能
- 适合资源受限的边缘设备联邦学习场景
联邦学习（FL）在分布式学习中前景广阔，但其复杂性导致客户端计算能耗显著增加。这一问题在能量采集联邦学习（EHFL）系统中尤为严峻，因设备可用性受有限且时变能量资源制约。本文提出FedBacys，一种基于电池状态的联邦学习框架，通过根据用户电池水平设计循环客户端参与机制，实现客户端在预定传输时间前仅执行必要本地训练。该方法通过客户端分组与顺序调度，减少冗余计算，降低系统整体能耗，并提升学习稳定性。实验表明，相较于现有方法，FedBacys在能效和性能一致性方面均有显著提升，即使在非独立同分布（non-i.i.d.）数据分布和极低充电频率条件下仍表现稳健。本工作首次全面评估了周期性客户端参与在EHFL中的应用，将通信与计算成本统一纳入资源感知调度策略。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a promising framework for distributed learning, but its growing complexity has led to significant energy consumption, particularly from computations on the client side. This challenge is especially critical in energy-harvesting FL (EHFL) systems, where device availability fluctuates due to limited and time-varying energy resources. We propose FedBacys, a battery-aware FL framework that introduces cyclic client participation based on users' battery levels to cope with these issues. FedBacys enables clients to save energy and strategically perform local training just before their designated transmission time by clustering clients and scheduling their involvement sequentially. This design minimizes redundant computation, reduces system-wide energy usage, and improves learning stability. Our experiments demonstrate that FedBacys outperforms existing approaches in terms of energy efficiency and performance consistency, exhibiting robustness even under non-i.i.d. training data distributions and with very infrequent battery charging. This work presents the first comprehensive evaluation of cyclic client participation in EHFL, incorporating both communication and computation costs into a unified, resource-aware scheduling strategy.
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