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Hierarchical Aggregation for Federated Learning in Heterogeneous IoT Scenarios: Enhancing Privacy and Communication Efficiency

Author

Listed:
  • Chen Qiu

    (Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan)

  • Ziang Wu

    (Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan)

  • Haoda Wang

    (Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan)

  • Qinglin Yang

    (Cyberspace Institute of Advanced Technology/Huangpu Research School of Guangzhou University, Guangzhou University (Huangpu), Guangzhou 510006, China)

  • Yu Wang

    (Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Chunhua Su

    (Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan
    These authors contributed equally to this work.)

Abstract

Federated Learning (FL) is a distributed machine-learning paradigm that enables models to be trained across multiple decentralized devices or servers holding local data without transferring the raw data to a central location. However, applying FL to heterogeneous IoT scenarios comes with several challenges due to the diverse nature of these devices in terms of hardware capabilities, communications, and data heterogeneity. Furthermore, the conventional parameter server-based FL paradigm aggregates the trained parameters of devices directly, which incurs high communication overhead. To this end, this paper designs a hierarchical federated-learning framework for heterogeneous IoT systems, focusing on enhancing communication efficiency and ensuring data security through lightweight encryption. By leveraging hierarchical aggregation, lightweight stream encryption, and adaptive device participation, the proposed framework provides an efficient and robust solution for federated learning in dynamic and resource-constrained IoT environments. The extensive experimental results show that the proposed FL paradigm significantly reduces round time by 20%.

Suggested Citation

  • Chen Qiu & Ziang Wu & Haoda Wang & Qinglin Yang & Yu Wang & Chunhua Su, 2025. "Hierarchical Aggregation for Federated Learning in Heterogeneous IoT Scenarios: Enhancing Privacy and Communication Efficiency," Future Internet, MDPI, vol. 17(1), pages 1-25, January.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:1:p:18-:d:1560543
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    References listed on IDEAS

    as
    1. Panagiotis Gkonis & Anastasios Giannopoulos & Panagiotis Trakadas & Xavi Masip-Bruin & Francesco D’Andria, 2023. "A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues," Future Internet, MDPI, vol. 15(12), pages 1-27, November.
    2. Rezak Aziz & Soumya Banerjee & Samia Bouzefrane & Thinh Le Vinh, 2023. "Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm," Future Internet, MDPI, vol. 15(9), pages 1-25, September.
    Full references (including those not matched with items on IDEAS)

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