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.

Wireless propagation is conventionally considered as the enabling tool for transporting information in digital communications. However, recent research has shown that the perturbations of the same electromagnetic (EM) fields that are adopted for data transmission can be used as a powerful sensing tool for device-free radio vision. Applications range from human body motion detection and localization to passive gesture recognition. In line with the current evolution of mobile phone sensing [1], radio terminals are not only ubiquitous communication interfaces, but they also incorporate novel or augmented sensing potential, capable of acquiring an accurate human-scale understanding of space and motion. This article shows how radio-frequency (RF) signals can be employed to provide a device-free environmental vision and investigates the detection and tracking capabilities for potential benefits in daily life

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