Resource optimizing federated learning for use with IoT: A systematic review

Recently, Federated Learning (FL) has been explored as a new paradigm that preserves both data privacy and end-users knowledge while reducing latency during model training. While often applied between a central server and the edge devices, FL deals with a wide range of end-user applications and devices. Given the Internet of Things (IoT) current popularity, its relevance and its penetration in several new application domains, this work examines the new challenges faced when combining IoT with the classic FL model. The limited resources of IoT edge devices require careful adaptation of the way how FL should be structured in this scenario. In addition, since FL is a distributed paradigm that shares deep learning artifacts through a network, there are also communication issues reminiscent to IoT networks that need special consideration. Thus, it is necessary to optimize the use of both processing and communication resources when considering the use of IoT edge devices as part of a FL. This paper systematically reviews current advances and steps taken towards dealing with resource optimization when using FL as part of IoT scenarios. We examine the published works over the last ten years and discuss the main goals, scenarios, and developed solutions to solve the problems encountered. We also present the main metrics used to quantify the effectiveness and to evaluate the performance of existing IoT based architectures with FL support.