A Novel Edge-based Multi-Layer Hierarchical Architecture for Federated Learning

In the last few years, Internet of Things (IoT) devices are multiplicating their presence in our daily life. This means that the data generated in our houses, offices, and common places is starting to be too big to be elaborated in a limited number of places. In this scenario, the advent of Edge Computing, in general, and Edge Intelligence, in particular, is favoring the scalability and the efficiency of IoT systems. Such paradigms allow, by using devices placed at the edge of the network, the distributed elaboration of data created by IoT devices so permitting to transmit to the cloud only synthetic information. Edge Intelligence supports the so-called Federated Learning (FL), which is a novel paradigm that allows the distributed training of neural network models. Such models are initially distributed from the cloud to edge nodes and, on such edge nodes, they are refined based on data gathered from IoT nodes. Such refined models are sent back to the cloud and merged with other models elaborated on different edge nodes. \\ This paper presents a novel architecture for Federated Learning enabling a Multi-Layer Hierarchical Federated Learning (MLH-FL) that allows to execute the traditional FL with model aggregation at different layers. The proposed approach will be also evaluated with some simulations and the final accuracy and loss of the obtained models will be compared with the traditional FL approach.