Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet-of-Things (IoT) networks. This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.

翻译：联邦学习使无线终端能够协同学习共享参数模型，同时将所有训练数据保留在设备本身。参数共享包括同步和异步两种方式：前者以块或帧的形式传输参数并等待所有传输完成，后者则提供待处理及失败的参数传输请求的状态信息。无论采用同步还是异步参数共享，学习模型都需要适应不同的网络架构，因为在资源受限的大规模物联网网络中，不恰当的学习模型会降低学习性能，甚至导致异步传输时的模型发散。本文针对资源受限的分布式物联网网络，提出了一个去中心化学习模型，并开发了一种异步参数共享算法。该去中心化学习模型在节点数量增加时趋近于凸函数，其学习过程收敛到全局驻点的概率高于集中式联邦学习模型。进一步，通过联合考虑联邦学习的收敛界限与无线通信的传输时延，我们开发了一种节点调度和带宽分配算法以最小化传输时延。大量仿真结果证实了该分布式算法在学习模型快速收敛与低传输时延方面的有效性。