无线联邦学习中用多时尺度差分编码，提升模型广播的鲁棒性和通信效率。
Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning
- 引入双层差分编码，结合长、中、短三类更新周期，实现高效模型分发。
- 即使丢失部分更新，设备也能在全量模型间重建最新模型，降低失败影响。
- 适用于存在链路中断的无线联邦学习场景，适合通信资源受限的部署环境。
在标准联邦学习系统中，参数服务器在每轮迭代中向参与设备广播全局模型。受连续全局模型间时间相关性启发，可对全局模型分发采用差分编码以减少信息量，从而实现更低量化位数的通信。然而，由于无线链路故障，设备可能偶尔错过差分更新，导致无法重建全局模型，只能基于过时模型继续本地训练或保持空闲，直至下一轮全量模型广播。为此，本文提出一种混合时尺度差分编码（MTDC）方案，通过调整参考模型，在两个不同层级上进行差分编码。在该方案下，设备即使遗漏一次差分更新，仍可在两次全量模型广播之间重构最新全局模型。我们提供了收敛性分析，据此设计了感知年龄的变体，并提出了设备调度策略以进一步提升通信效率。仿真结果表明，在存在下行传输失败的情况下，所提MTDC方案在相似通信资源预算下，相较基线方法展现出更优的学习性能。
原文摘要 · Abstract (English)
In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration. Motivated by the temporal correlation between consecutive global models, differential coding can be applied to global model dissemination to reduce the information magnitude, thereby enabling communication with fewer quantization bits. However, due to wireless link failures, devices may occasionally miss differential updates and consequently fail to reconstruct the global model. As a result, they either continue local training based on an outdated model or remain idle until the next full-model broadcast becomes available. To address this challenge, we propose a mixed-timescale differential coding (MTDC) scheme that performs differential coding at two different levels by adjusting the reference model. With MTDC, a device can reconstruct the latest global model between two full-model broadcasts even if it misses a differential update. We provide a convergence analysis that motivates the design of an age-aware variant of MTDC, along with a device scheduling policy to further improve communication efficiency. Simulation results demonstrate that the proposed MTDC schemes achieve superior learning performance compared to baseline methods under similar communication resource budgets in the presence of downlink transmission failures.
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