  Federated learning (FL) is emerging as a new paradigm to train machine
learning models in distributed systems. Rather than sharing, and disclosing,
the training dataset with the server, the model parameters (e.g. neural
networks weights and biases) are optimized collectively by large populations of
interconnected devices, acting as local learners. FL can be applied to
power-constrained IoT devices with slow and sporadic connections. In addition,
it does not need data to be exported to third parties, preserving privacy.
Despite these benefits, a main limit of existing approaches is the centralized
optimization which relies on a server for aggregation and fusion of local
parameters; this has the drawback of a single point of failure and scaling
issues for increasing network size. The paper proposes a fully distributed (or
server-less) learning approach: the proposed FL algorithms leverage the
cooperation of devices that perform data operations inside the network by
iterating local computations and mutual interactions via consensus-based
methods. The approach lays the groundwork for integration of FL within 5G and
beyond networks characterized by decentralized connectivity and computing, with
intelligence distributed over the end-devices. The proposed methodology is
verified by experimental datasets collected inside an industrial IoT
environment.

Signal Processing;Distributed, Parallel, and Cluster Computing;Machine Learning