Understanding global aggregation and optimization of federated learning

We investigate the hypothesis that exploring Federated Learning (FL) aggregation methods can enhance training processes within FL frameworks, particularly in resource-constrained edge networks. The methodology employed involved a thorough review of existing FL aggregation methods, leveraging literature databases for data collection and algorithmic simulations for analysis. Distinct taxonomies were introduced to dissect the accuracy and behaviors of these methods. Results revealed critical issues such as communication constraints, personalization, and fairness within FL, emphasizing the necessity for detailed investigations to bridge theory and application gaps. Through meticulous examination and comparative analyses of existing aggregation methods, we provide valuable insights into the development of resilient FL aggregators, laying the groundwork for future advancements in the field.