利用下行带宽提升联邦学习更新效率，显著加快训练速度与精度。
Efficient Federated Learning with Timely Update Dissemination
- 通过异步/同步框架结合动态聚合与自适应调整机制，实现高效模型更新。
- 在六种模型、五大数据集上，精度最高提升145.87%，效率最高提升97.59%。
- 适合对通信效率与训练速度敏感的分布式智能场景应用。
联邦学习（FL）已成为管理分布式数据的重要方法，近年进展显著。本文提出一种高效联邦学习方法，充分利用额外的下行带宽资源，确保模型更新及时传播。首先，在异步框架下引入异步延迟感知模型更新（FedASMU），融合服务器端与设备端策略：服务器端采用动态模型聚合技术，协调本地更新与全局模型，提升准确率与效率；设备端则设计自适应模型调整机制，在训练中融合最新全局模型与本地模型，进一步提高精度。随后，将该方法扩展至同步场景，称为FedSSMU。理论分析证明了方法的收敛性。大量实验涵盖六种模型与五个公开数据集，结果表明FedASMU与FedSSMU在准确率（最高提升145.87%）和效率（最高提升97.59%）方面均显著优于基线方法。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a compelling methodology for the management of distributed data, marked by significant advancements in recent years. In this paper, we propose an efficient FL approach that capitalizes on additional downlink bandwidth resources to ensure timely update dissemination. Initially, we implement this strategy within an asynchronous framework, introducing the Asynchronous Staleness-aware Model Update (FedASMU), which integrates both server-side and device-side methodologies. On the server side, we present an asynchronous FL system model that employs a dynamic model aggregation technique, which harmonizes local model updates with the global model to enhance both accuracy and efficiency. Concurrently, on the device side, we propose an adaptive model adjustment mechanism that integrates the latest global model with local models during training to further elevate accuracy. Subsequently, we extend this approach to a synchronous context, referred to as FedSSMU. Theoretical analyses substantiate the convergence of our proposed methodologies. Extensive experiments, encompassing six models and five public datasets, demonstrate that FedASMU and FedSSMU significantly surpass baseline methods in terms of both accuracy (up to 145.87%) and efficiency (up to 97.59%).
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