  Clustered Federated Multitask Learning (CFL) was introduced as an efficient
scheme to obtain reliable specialized models when data is imbalanced and
distributed in a non-i.i.d. (non-independent and identically distributed)
fashion amongst clients. While a similarity measure metric, like the cosine
similarity, can be used to endow groups of the client with a specialized model,
this process can be arduous as the server should involve all clients in each of
the federated learning rounds. Therefore, it is imperative that a subset of
clients is selected periodically due to the limited bandwidth and latency
constraints at the network edge. To this end, this paper proposes a new client
selection algorithm that aims to accelerate the convergence rate for obtaining
specialized machine learning models that achieve high test accuracies for all
client groups. Specifically, we introduce a client selection approach that
leverages the devices' heterogeneity to schedule the clients based on their
round latency and exploits the bandwidth reuse for clients that consume more
time to update the model. Then, the server performs model averaging and
clusters the clients based on predefined thresholds. When a specific cluster
reaches a stationary point, the proposed algorithm uses a greedy scheduling
algorithm for that group by selecting the clients with less latency to update
the model. Extensive experiments show that the proposed approach lowers the
training time and accelerates the convergence rate by up to 50% while imbuing
each client with a specialized model that is fit for its local data
distribution.
