面向任务的通信高效联邦学习框架：从孤立优化到整体协同

中文摘要 AI 辅助

通信瓶颈仍然是联邦学习（FL）大规模部署的主要障碍。本文提出了一种构建通信高效联邦学习的综合框架，该框架基于三个基本支柱：模型压缩、客户端选择和资源分配。我们首先综述了每个支柱的最新技术，具体阐明了量化、剪枝和低秩近似如何减少有效载荷；智能客户端调度器如何利用异构性；以及集成感知与通信（ISAC）、空中计算（AirComp）等新兴通信范式如何重新定义带宽和能量利用。随后，我们通过面向任务的设计理念将这些见解统一起来，该理念将策略选择与跨层多目标优化相结合。为了验证所提出的框架，我们开展了一个自动驾驶案例研究，包含两个互补实验：一是面向任务的客户端调度策略，在相同通信时间预算下提高了目标检测准确率；二是联合量化-带宽优化，在动态网络下进一步减少了总训练时间。这些实验共同证明了面向任务的整体设计在实际联邦学习部署中的优势。

英文摘要

Communication bottlenecks remain a primary obstacle to the large-scale deployment of federated learning (FL). This article proposes a comprehensive framework for building communication-efficient FL, founded on three fundamental pillars: model compression, client selection, and resource allocation. We first survey state-of-the-art techniques for each pillar, specifically elucidating how quantization, pruning, and low-rank approximation reduce payloads; how intelligent client schedulers exploit heterogeneity; and how emerging communication paradigms such as Integrated Sensing and Communication (ISAC) and Over-the-Air Computation (AirComp) redefine bandwidth and energy utilization. Subsequently, these insights are unified through a task-oriented design philosophy that couples strategy selection with cross-layer, multi-objective optimization. To validate the proposed framework, we present an autonomous driving case study with two complementary experiments: a task-oriented client scheduling strategy that improves object detection accuracy under the same communication time budget, and a joint quantization-bandwidth optimization that further reduces total training time under dynamic networks. Together, the experiments demonstrate the advantages of holistic task-oriented design for real-world FL deployment.

发表机构

• Tongji University（同济大学）
• Queen Mary University of London（伦敦大学玛丽女王学院）

机构由 AI 辅助整理，请以论文原文为准。