Guest Editorial: Special Section on the Latest Developments in Federated Learning for the Management of Networked Systems and Resources

Azzam Mourad, Hadi Otrok, Ernesto Damiani, Mérouane Debbah, Nadra Guizani, Shiqiang Wang, Guangjie Han, Rabeb Mizouni, Jamal Bentahar, Chamseddine Talhi

Abstract: Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growing complexity and heterogeneity of modern systems. In this context, Federated Learning (FL) has gained increasing interest as a decentralized approach that overcomes the limitations of centralized systems for data analysis.