Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks

TL;DR

This paper introduces a fully distributed federated learning approach for massive IoT networks, enabling cooperative model training without a central server, thus enhancing scalability and robustness in decentralized environments.

Contribution

It proposes a novel consensus-based federated learning algorithm that operates in a fully distributed manner, suitable for large-scale IoT networks with decentralized connectivity.

Findings

Effective in industrial IoT environments

Scalable without centralized server dependency

Preserves data privacy during training

Abstract

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.