The DOI matches its Crossref record; 248 of the 252 references it cites were found in a registry or an index.
Federated Learning for IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions
Open accessAbstract
The rapid advancement of Internet of Things (IoT) technology has transformed the digital landscape, enabling unprecedented connectivity between devices, people, and services. Traditionally, IoT-generated data was processed through centralized, cloud-based machine learning (ML) systems, raising significant privacy, security, and network bandwidth concerns. Federated Learning (FL) presents a viable alternative by transmitting only model parameters while preserving local data privacy. Despite the growing body of research, there remains a gap in comprehensive studies on FL-enabled IoT systems. This review provides an in-depth examination of the integration of FL with IoT, highlighting how FL enhances the efficiency, robustness, and adaptability of IoT systems. The paper introduces the foundational principles of FL, followed by an exploration of its key benefits in decentralized IoT applications. It presents a comparative analysis of FL-IoT architectures using quantitative metrics and proposes a taxonomy that clarifies the complexities and variations in FL-enabled IoT systems. The challenges of deploying FL in IoT environments are discussed, along with current trends and solutions aimed at overcoming these hurdles. Furthermore, the review explores the integration of FL with emerging technologies, including foundational models (FMs), green and sustainable 6th-generation (6G) IoT networks, and deep reinforcement learning (DRL), emphasizing their role in enhancing FL’s efficiency and resilience. It also covers FL frameworks and benchmarks, providing a valuable resource for researchers and practitioners in the field The article concludes by identifying promising research directions that are expected to drive future advancements in this dynamic and expanding field.
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“The need for high-performance hardware limits the deployment of FL in smaller healthcare facilities and remote areas, restricting access to advanced healthcare technologies in resource-constrained systems [115].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [115]
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Metadata
- Authors
- Adam, M., & Baroudi, U.
- Affiliations
- King Fahd University of Petroleum and Minerals (SA)
- Venue
- IEEE Open Journal of the Communications Society
- Published
- 2024
- Volume
- 5
- Pages
- 7842-7877
- ISSN
- 2644-125X (ISSN-L)
- OpenAlex
- W4404688019
- Cited by
- 46
- References
- 252
- Citations by year
- 2026: 28 · 2025: 17 · 2024: 1
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Open Journal of the Communications Society (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), KFUPM University, Dhahran, Saudi Arabia (INSS2407)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- federated learning, Internet of Things, privacy-preserving machine learning, decentralized learning, 6G, green Internet of Things, deep reinforcement learning, large language models, data privacy
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