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Clustered federated Multitask learning is introduced as an efficienttechnique when data is unbalanced and distributed amongst clients in anon-independent and identically distributed manner. While a similarity metriccan provide client groups with specialized models according to their datadistribution, this process can be time-consuming because the server needs tocapture all data distribution first from all clients to perform the correctclustering. Due to resource and time constraints at the network edge, only afraction of devices {is} selected every round, necessitating the need for anefficient scheduling technique to address these issues. Thus, this paperintroduces a two-phased client selection and scheduling approach to improve theconvergence speed while capturing all data distributions. This approach ensurescorrect clustering and fairness between clients by leveraging bandwidth reusefor participants spent a longer time training their models and exploiting theheterogeneity in the devices to schedule the participants according to theirdelay. The server then performs the clustering depending on predeterminedthresholds and stopping criteria. When a specified cluster approximates astopping point, the server employs a greedy selection for that cluster bypicking the devices with lower delay and better resources. The convergenceanalysis is provided, showing the relationship between the proposed schedulingapproach and the convergence rate of the specialized models to obtainconvergence bounds under non-i.i.d. data distribution. We carry out extensivesimulations, and the results demonstrate that the proposed algorithms reducetraining time and improve the convergence speed while equipping every user witha customized model tailored to its data distribution.

Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning