With the advance of the powerful heterogeneous, parallel and distributed
computing systems and ever increasing immense amount of data, machine learning
has become an indispensable part of cutting-edge technology, scientific
research and consumer products. In this study, we present a re
通过使用一种叫作 Fog learning 的新学习范式,从边缘设备到云服务器中智能地分布机器学习模型的训练,来增强联邦学习的三个重要维度:网络、异构性和邻近性,并考虑由各种接近程度异构设备组成的多层混合学习框架,通过设备到设备(D2D)通信进行协作学习,使其从联邦学习中用于参数传输的星型网络拓扑迁移到更分布式的规模。