Abstract
Internet of Things (IoT) services will use machine learning tools to
efficiently analyze various types of data collected by IoT devices for
inference, autonomy, and control purposes. However, due to resource constraints
and privacy challenges, edge IoT devices may not be able to transmit their
collected data to a central controller for training machine learning models. To
overcome this challenge, federated learning (FL) has been proposed as a means
for enabling edge devices to train a shared machine learning model without data
exchanges thus reducing communication overhead and preserving data privacy.
However, Google's seminal FL algorithm requires all devices to be directly
connected with a central controller, which significantly limits its application
scenarios. In this context, this paper introduces a novel FL framework, called
collaborative FL (CFL), which enables edge devices to implement FL with less
reliance on a central controller. The fundamentals of this framework are
developed and then, a number of communication techniques are proposed so as to
improve the performance of CFL. To this end, an overview of centralized
learning, Google's seminal FL, and CFL is first presented. For each type of
learning, the basic architecture as well as its advantages, drawbacks, and
usage conditions are introduced. Then, three CFL performance metrics are
presented and a suite of communication techniques ranging from network
formation, device scheduling, mobility management, and coding is introduced to
optimize the performance of CFL. For each technique, future research
opportunities are also discussed. In a nutshell, this article will showcase how
the proposed CFL framework can be effectively implemented at the edge of
large-scale wireless systems such as the Internet of Things.