Federated learning framework to decentralize mobility forecasting in smart cities scenarios

• Autor(a) principal: Valente, Renato Lima
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/38983
• topic: Federated learning; Distributed forecasting; Smart city; FL on edge devices

The new Federated Learning (FL) paradigm has several performance advantages over centralized models. It has lower latency and communication overhead when doing most of the processing on the edge devices, it improves the privacy as data does not travel over the network, it facilitates the handling of heterogeneous data sources and expands scalability. However, the development of FL-based solutions is done through tools usually aimed for specialists as it always requires some programming. To cover this gap, this dissertation proposes a lightweight container-based framework that does not require programming knowledge or experience with machine learning models from its users. This framework, denoted as FedFramework, offers a range of machine learning (ML) algorithms that support the build of prediction engines for edge devices as well as making key algorithms/models available for aggregation and model refinement on the central server. We demonstrate the efficiency of the proposed framework in estimating vehicle mobility in and out of the city, using real data collected by the Aveiro Tech City Living Lab communications infrastructure of the Aveiro city. Moreover, a testbed that integrates the components of the city infrastructure was implemented, where edge devices (Jetsons Nano and Jetson Xavier) are connected to a cloud server. The FedFramework was deployed in this testbed, where its portability, its scalability in devices with few resources, its performance, the impact on the communication between the edge and the server, and the consumption of resources were evaluated.