  The ongoing deployment of the Internet of Things (IoT)-based smart
applications is spurring the adoption of machine learning as a key technology
enabler. To overcome the privacy and overhead challenges of centralized machine
learning, there has been a significant recent interest in the concept of
federated learning. Federated learning offers on-device, privacy-preserving
machine learning without the need to transfer end-devices data to a third party
location. However, federated learning still has privacy concerns due to
sensitive information inferring capability of the aggregation server using
end-devices local learning models. Furthermore, the federated learning process
might fail due to a failure in the aggregation server (e.g., due to a malicious
attack or physical defect). Other than privacy and robustness issues, federated
learning over IoT networks requires a significant amount of communication
resources for training. To cope with these issues, we propose a novel concept
of dispersed federated learning (DFL) that is based on the true
decentralization. We opine that DFL will serve as a practical implementation of
federated learning for various IoT-based smart applications such as smart
industries and intelligent transportation systems. First, the fundamentals of
the DFL are presented. Second, a taxonomy is devised with a qualitative
analysis of various DFL schemes. Third, a DFL framework for IoT networks is
proposed with a matching theory-based solution. Finally, an outlook on future
research directions is presented.
