J. Abouei

FedMetaOpt: a comprehensive survey of metaheuristic optimization for federated learning in client selection, resource allocation, and model aggregation

EDAF: An Enhanced Dual-Alignment Framework for Robust Federated Learning in Heterogeneous IoT Environments

Federated learning (FL) in real-world deployments is fundamentally challenged by heterogeneous environments, where clients differ in both model architectures and computational capacities (system heterogeneity), as well as in local data distributions (statistical heterogeneity due to nonindependent and nonidentically di...

The GAO-based federated learning framework with adaptive client selection for resource-efficient edge-IoT systems

The Federated Green Anaconda Optimizer (FedGAO), an innovative FL framework inspired by the behavioral patterns of the Green Anaconda Optimizer (GAO), is proposed, demonstrating superior performance in terms of accuracy, convergence speed, and resource efficiency.