  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.
