Global Model

Global model training in federated learning aims to collaboratively build a single, shared model from decentralized data sources while preserving privacy. Current research emphasizes addressing challenges like data heterogeneity through techniques such as model aggregation strategies (e.g., FedAvg variants, aggregation-free approaches), personalized model adaptation, and efficient model compression to reduce communication overhead. These advancements are crucial for improving the accuracy and robustness of global models in diverse applications, ranging from climate modeling and medical imaging to resource-constrained edge computing environments.