Federated Learning via Indirect Server-Client Communications
Federated Learning via Indirect Server-Client Communications
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DOI：
10.1109/ciss56502.2023.10089783
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Jieming Bian;Cong Shen;Jie Xu
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文献类型：
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作者：
Jieming Bian;Cong Shen;Jie Xu
联邦学习（FL）是一种通信高效和隐私保护的分布式机器学习框架，最近获得了大量的研究关注。尽管FL算法的形式不同（例如，同步FL、异步FL）和底层优化方法，几乎所有现有的工作都隐含地假设了通信基础设施的存在，该通信基础设施促进了服务器和客户端之间用于模型数据交换的直接通信。然而，这种假设在许多可以受益于分布式学习但缺乏适当的通信基础设施（例如，在偏远地区的智能传感）。在本文中，我们提出了一种新的FL框架，名为FedEx（FL via Model Express Delivery的缩写），它利用移动的运输工具（例如，无人机）来建立服务器和客户端之间的间接通信信道。两个算法，称为联邦快递同步和联邦快递异步，开发取决于是否采用同步或同步调度的运输商。即使间接通信引入异构延迟的客户端的全球模型传播和本地模型收集，我们证明了联邦快递的两个版本的收敛。收敛性分析随后揭示了如何将客户端分配给不同的传输器并设计客户端之间的路由。通过在两个公共数据集上的模拟网络中的实验来评估联邦快递的性能。
Federated Learning (FL) is a communication-efficient and privacy-preserving distributed machine learning framework that has gained a significant amount of research attention recently. Despite the different forms of FL algorithms (e.g., synchronous FL, asynchronous FL) and the underlying optimization methods, nearly all existing works implicitly assumed the existence of a communication infrastructure that facilitates the direct communication between the server and the clients for the model data exchange. This assumption, however, does not hold in many real-world applications that can benefit from distributed learning but lack a proper communication infrastructure (e.g., smart sensing in remote areas). In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e.g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients. Two algorithms, called FedEx-Sync and FedEx-Async, are developed depending on whether the transporters adopt a synchronized or an asynchronized schedule. Even though the indirect communications introduce heterogeneous delays to clients for both the global model dissemination and the local model collection, we prove the convergence of both versions of FedEx. The convergence analysis subsequently sheds lights on how to assign clients to different transporters and design the routes among the clients. The performance of FedEx is evaluated through experiments in a simulated network on two public datasets.