Intelligent Model Aggregation in Hierarchical Clustered Federated Multitask Learning

Clustered federated multi task learning (CFL) is introduced as an effective and efficient approach for addressing statistical challenges such as non-independent and identically distributed (non-IID) data among workers. Workers in CFL are clustered in groups based on similarity (i.e., cosine similarity) in their data distributions, in which each cluster is equipped with an efficient specialized model. However, this approach can be costly and time-consuming when implemented in hierarchical wireless networks (HWNs) due to uploading several models at every round to enable the cloud server to capture the incongruent data distribution from different edge networks. This brings about the need for novel solutions to address these challenges. To this end, this paper introduces a framework with two cloud-based model aggregation approaches, round-based and split-based, so as to minimize latency and resource consumption while attaining satisfying personalized accuracy. In the round-based scheme, the cloud aggregates the models from the edge servers after a predetermined number of rounds. As for the split-based scheme, the models are collected by the cloud only when edge servers perform the split. Extensive experiments are conducted to evaluate and compare the proposed heuristics against approaches presented in the recent literature. The numerical results and findings demonstrate that the proposed heuristics significantly conserve resources by reducing energy consumption by 60% and saving time, all while accelerating the convergence rate for cluster workers across various edge networks.