Machine learning (ML) tasks are becoming ubiquitous in today's network applications. Federated learning has emerged recently as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices. To address this, we advocate a new learning paradigm called fog learning, which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers. Fog learning enhances federated learning along three major dimensions: network, heterogeneity, and proximity. It considers a multi-layer hybrid learning framework consisting of heterogeneous devices with various proximities. It accounts for the topology structures of the local networks among the heterogeneous nodes at each network layer, orchestrating them for collaborative/cooperative learning through device-to-device communications. This migrates from star network topologies used for parameter transfers in federated learning to more distributed topologies at scale. We discuss several open research directions toward realizing fog learning.

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从联合到雾学习：异构无线网络上的分布式机器学习

机器学习 (ML) 任务在当今的网络应用中变得无处不在。联邦学习最近成为一种通过利用跨节点收集数据的处理能力在网络边缘训练 ML 模型的技术。由于跨设备存在的计算和通信能力的显着异质性，在当代网络中采用传统的联邦学习存在一些挑战。为了解决这个问题，我们提倡一种称为雾学习的新学习范式，它将在从边缘设备到云服务器的连续节点上智能地分配 ML 模型训练。雾学习从三个主要维度增强了联邦学习：网络、异构性和邻近性。它考虑了一个由具有各种邻近性的异构设备组成的多层混合学习框架。它考虑了每个网络层异构节点之间本地网络的拓扑结构，通过设备到设备的通信协调它们以进行协作/协作学习。这从用于联邦学习中参数传输的星型网络拓扑迁移到更大规模的分布式拓扑。我们讨论了实现雾学习的几个开放研究方向。这从用于联邦学习中参数传输的星型网络拓扑迁移到更大规模的分布式拓扑。我们讨论了实现雾学习的几个开放研究方向。这从用于联邦学习中参数传输的星型网络拓扑迁移到更大规模的分布式拓扑。我们讨论了实现雾学习的几个开放研究方向。