Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-to-server (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integration of unlabeled data and heterogeneous device privacy into ML model training. Finally, we describe some open research directions at the intersection of cooperative edge/fog and FL.

翻译：联邦学习（FL）已被推广为一种在边缘/雾网络上训练机器学习（ML）模型的流行技术。传统的FL实现大多忽略了网络间协作的潜力，将参与ML的边缘/雾设备及其他基础设施视为独立的处理单元。因此，FL容易受到网络异构性多个维度的影响，例如计算能力、通信资源、数据质量和隐私需求的变化。我们倡导协作联邦学习（CFL），这是一种基于设备到设备（D2D）和设备到服务器（D2S）交互的协作式边缘/雾ML范式。通过D2D和D2S协作，CFL通过启用模型/数据/资源池化机制来抵消边缘/雾网络中的网络异构性，这将显著提升ML模型训练质量和网络资源消耗。我们提出了一套构成D2D和D2S协作基础的核心方法，并展示了初步实验以证明其优势。我们还讨论了该协作框架所赋能的新FL功能，例如将未标注数据和异构设备隐私整合到ML模型训练中。最后，我们描述了在协作边缘/雾与FL交叉领域的一些开放研究方向。