@Article{cmc.2025.068319,
AUTHOR = {Imran Ahmed, Misbah Ahmad, Gwanggil Jeon},
TITLE = {Federated Learning in Convergence ICT: A Systematic Review on Recent Advancements, Challenges, and Future Directions},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {3},
PAGES = {4237--4273},
URL = {http://www.techscience.com/cmc/v85n3/64177},
ISSN = {1546-2226},
ABSTRACT = {The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces challenges, including communication overhead, heterogeneity, security vulnerabilities, and limited edge resources. While recent studies have addressed these issues individually, the literature lacks a unified, cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT. This systematic review offers a comprehensive, cross-domain examination of FL within converged ICT infrastructures. The central research question guiding this review is: How can FL be effectively integrated into Convergence ICT environments, and what are the main challenges in implementing FL in such environments, along with possible solutions? We begin with a foundational overview of FL concepts and classifications, followed by a detailed taxonomy of FL architectures, learning strategies, and privacy-preserving mechanisms. Through in-depth case studies, we analyse FL’s application across diverse verticals, including smart cities, healthcare, industrial automation, and autonomous systems. We further identify critical challenges—such as system and data heterogeneity, limited edge resources, and security vulnerabilities—and review state-of-the-art mitigation strategies, including edge-aware optimization, secure aggregation, and adaptive model updates. In addition, we explore emerging directions in FL research, such as energy-efficient learning, federated reinforcement learning, and integration with blockchain, quantum computing, and self-adaptive networks. This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable, secure, and sustainable FL deployment in future ICT ecosystems.},
DOI = {10.32604/cmc.2025.068319}
}