通过交替使用联邦学习与模型蒸馏，显著降低物联网设备能耗与通信开销。
Federated Learning-Distillation Alternation for Resource-Constrained IoT
- 设备在联邦学习与模型蒸馏间轮换，平衡精度与资源消耗。
- 相比传统联邦学习，能耗降低最高达98%，收敛更快。
- 适合能量采集、无线干扰严重的低资源物联网场景。
联邦学习（FL）在物联网（IoT）网络中面临设备能源与通信资源受限的挑战，尤其当模型规模较大时。若设备依赖能量采集（EH），能量供应随时间波动，影响每轮参与设备的平均数量。此外，大型模型更新在共享无线环境中易受无关背景流量干扰。作为替代方案，联邦蒸馏（FD）通过传输本地模型输出（远小于完整模型）减少通信开销与能耗，但会牺牲模型精度。为此，本文提出联邦学习-蒸馏交替（FLDA）机制。在多信道时隙ALOHA EH-IoT网络中，考虑背景干扰，FLDA在每轮中交替执行FD与FL，实现信息量与低开销的平衡。实验表明，相较于FL与FD，FLDA在模型精度上更高，收敛速度更快；同时在达成目标精度时，能耗节省最高达98%，且对干扰更不敏感。该方法适用于资源受限、存在能量波动与无线干扰的物联网环境。
原文摘要 · Abstract (English)
Federated learning (FL) faces significant challenges in Internet of Things (IoT) networks due to device limitations in energy and communication resources, especially when considering the large size of FL models. From an energy perspective, the challenge is aggravated if devices rely on energy harvesting (EH), as energy availability can vary significantly over time, influencing the average number of participating users in each iteration. Additionally, the transmission of large model updates is more susceptible to interference from uncorrelated background traffic in shared wireless environments. As an alternative, federated distillation (FD) reduces communication overhead and energy consumption by transmitting local model outputs, which are typically much smaller than the entire model used in FL. However, this comes at the cost of reduced model accuracy. Therefore, in this paper, we propose FL-distillation alternation (FLDA). In FLDA, devices alternate between FD and FL phases, balancing model information with lower communication overhead and energy consumption per iteration. We consider a multichannel slotted-ALOHA EH-IoT network subject to background traffic/interference. In such a scenario, FLDA demonstrates higher model accuracy than both FL and FD, and achieves faster convergence than FL. Moreover, FLDA achieves target accuracies saving up to 98% in energy consumption, while also being less sensitive to interference, both relative to FL.
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