  In multiple federated learning schemes, a random subset of clients sends in
each round their model updates to the server for aggregation. Although this
client selection strategy aims to reduce communication overhead, it remains
energy and computationally inefficient, especially when considering
resource-constrained devices as clients. This is because conventional random
client selection overlooks the content of exchanged information and falls short
of providing a mechanism to reduce the transmission of semantically redundant
data. To overcome this challenge, we propose clustering the clients with the
aid of similarity metrics, where a single client from each of the formed
clusters is selected in each round to participate in the federated training. To
evaluate our approach, we perform an extensive feasibility study considering
the use of nine statistical metrics in the clustering process. Simulation
results reveal that, when considering a scenario with high data heterogeneity
of clients, similarity-based clustering can reduce the number of required
rounds compared to the baseline random client selection. In addition, energy
consumption can be notably reduced from 23.93% to 41.61%, for those similarity
metrics with an equivalent number of clients per round as the baseline random
scheme.
