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

Chen Qiu, Ziang Wu, Haoda Wang, Qinglin Yang, Yu Wang and Chunhua Su ()
Additional contact information
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
Chunhua Su: Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan

Future Internet, 2025, vol. 17, issue 1, 1-25

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%.

Keywords: hierarchical federated learning; lightweight; heterogeneous device (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/1/18/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/1/18/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:1:p:18-:d:1560543

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().