The Internet of Things (IoT) has transformed how smart cities operate, significantly improving their inhabitants’ efficiency and overall quality of life. However, the massive volume of sensitive data generated by IoT devices presents challenges, including privacy concerns and communication overheads. Traditional centralized data processing can compromise privacy and require significant communication, leading to scalability problems and power drains. Federated Learning (FL) offers a solution by processing data locally and transmitting only the model parameters. Vanilla FL faces challenges in IoT environments due to latency, bandwidth constraints, and power drain. Hierarchical FL (HFL) effectively addresses these issues by hierarchically leveraging the processing capabilities of both cloud and edge servers to optimize resource utilization and efficiently minimize latency. This paper evaluates HFL using IoT-derived datasets, develops and implements the HFL framework for resource-constrained IoT systems, and conducts the first known HFL tests on relevant datasets to demonstrate its performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Performance Evaluation of Hierarchical Federated Learning on Resource-Constrained IoT Systems


    Beteiligte:
    Adam, Mumin (Autor:in) / Baroudi, Uthman (Autor:in)


    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    503922 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Hierarchical Federated Learning with Edge Optimization in Constrained Networks

    Zhang, Xiaoyang / Tham, Chen-Khong / Wang, Wenyi | IEEE | 2024



    Adaptive Transceiver Design for Wireless Hierarchical Federated Learning

    Zhou, Fangtong / Chen, Xu / Shan, Hangguan et al. | IEEE | 2023


    Toward Robust Hierarchical Federated Learning in Internet of Vehicles

    Zhou, Hongliang / Zheng, Yifeng / Huang, Hejiao et al. | IEEE | 2023


    Privacy-Preserving Resource Allocation for Asynchronous Federated Learning

    Chen, Xiaojing / Zhou, Zheer / Ni, Wei et al. | IEEE | 2024