无线联邦学习中下行链路模型广播的混合时间尺度差分编码
Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning
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中文总结 AI 辅助
针对无线联邦学习中因链路故障致设备错过差分更新无法重构全局模型的问题，提出混合时间尺度差分编码（MTDC）方案，通过调整参考模型在两级执行差分编码，配合收敛分析、年龄感知变体及调度策略，提升了学习性能。
中文摘要 AI 辅助
在标准联邦学习系统中，参数服务器每次迭代向参与设备广播全局模型。鉴于连续全局模型间的时间相关性，差分编码可用于全局模型传播以减少信息量，实现少比特量化通信。但因无线链路故障，设备可能错过差分更新而无法重构全局模型，只能基于过时模型继续本地训练或闲置。为此，我们提出混合时间尺度差分编码（MTDC）方案，通过调整参考模型在两个不同级别执行差分编码。即便错过差分更新，设备也能在两次全模型广播间重构最新全局模型。我们进行了收敛性分析，设计了MTDC的年龄感知变体及设备调度策略以提高通信效率。仿真结果表明，在下行传输失败且通信资源预算相似的情况下，所提MTDC方案比基线方法有更优学习性能。
英文摘要
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 As a result, they either continue local training based on an outdated model or remain idle until the next full-model broadcast becomes available.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.
发表机构
- Linköping University（林雪平大学）
机构由 AI 辅助整理，请以论文原文为准。
