… Our research underscores the potential of federated learning and deep learning in enhancing
… Adaptability to Different Network Types: The current testing of our model primarily focuses …

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability,
and training speed perspectives. Nonetheless, moving from centralized training to cross-…

… integrity, authenticity, and non-repudiation of model updates across the federated network. …
The adaptability of our framework to zero-day vulnerabilities lies in its decentralized …

… statistically heterogeneous federated learning environment … dynamic adaptive cluster
federated learning scheme (AICFL). … adaptability of AICFL to changes in the system environment. …

… and refine it in real time for adaptability to changing trends in dynamic environments. Our …
The source and target networks of our DRL are four layers of deep neural networks with …

Federated Learning (FL) enables collaborative and privacy-preserving training of machine
learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles …

… Federated learning (FL) presents an innovative framework facilitating collaborative ML …
Amidst the extensive rollout of the 5G network and the swift evolution of hardware capabilities, …

… it with the conventional federated learning approaches and other … to network architecture
as all these three variants achieve similar prediction results Thanks to its fast adaptability to …

… one of the major constraints on the application of federated learning (FL). To reduce the …
total communication time for FL in mobile networks. The proposed scheme can assign adaptive …

… (AHE) approach for privacy protection in federated learning. Mowla et al. [29] proposed a
federated learning scheme for intelligent jamming defense in flying ad-hoc networks. Liu et al. […