Federated Learning Protocols for IoT Edge Computing

In this article, we provide a set of federated learning (FL) protocols for future Internet architectures, which integrate the edge computing with the Internet of Things (IoT) known as "IoT edge computing." The proposed protocols aim to the efficient implementation of the FL, i.e., distributed intelligence, in future IoT networks, where edge computing will leverage the overall procedure at the edge of the network. We first provide a list of application requirements for such an FL implementation, which result in the architecture of constrained and nonconstrained IoT devices based on a set of Internet engineering task force (IETF) standards. The FL protocols consist of three stages as follows: 1) initial configuration; 2) distributed training; and 3) cloud updates. The specified FL protocols are tested using an experimental IoT platform, which is used to obtain experimental results that provide the performance evaluation of the FL protocols in terms of accuracy, time, and latency. We propose those FL protocols for the next-generation Internet (NGI), where IoT, edge computing, and FL will be blended efficiently for future Internet applications.