  We propose clustered federated multitask learning to address statistical
challenges in non-independent and identically distributed data across clients.
Our approach tackles complexities in hierarchical wireless networks by
clustering clients based on data distribution similarities and assigning
specialized models to each cluster. These complexities include slower
convergence and mismatched model allocation due to hierarchical model
aggregation and client selection. The proposed framework features a two-phase
client selection and a two-level model aggregation scheme. It ensures fairness
and effective participation using greedy and round-robin methods. Our approach
significantly enhances convergence speed, reduces training time, and decreases
energy consumption by up to 60%, ensuring clients receive models tailored to
their specific data needs.
