Client Clustering for Improved Federated Learning on Heterogeneous DataRadovič, Boris (Avtor) Pejović, Veljko (Mentor) federated learningdeep learningFederated learning (FL) is a distributed machine learning paradigm in which a model is collectively trained by using the data available on multiple devices without such devices exposing their data. This concept marks a significant stride towards decentralized AI. However, the challenge arises when dealing with non-independently and identically distributed (non-IID) data, as any kind of data heterogeneity among devices' datasets can hinder training convergence and worsen the predictive quality of the model being trained. Among the many techniques recently proposed for addressing such difficulties, there is clustering. Established clustering methods require the devices to possess a labelled dataset in order to assign the devices to a cluster, and this limits the applicability of such clustering approaches. In this thesis, we introduce a comprehensive framework and a suite of algorithms designed to cluster devices that lack a labelled dataset. Through experimentation, we demonstrate that our proposed algorithms yield results comparable to current state-of-the-art methods. An advantage of our approach is its capability to cluster devices that did not participate in the training stage. This includes cases where devices lack a labelled dataset or the devices' computational capabilities are limited.20232023-09-04 07:40:06Magistrsko delo/naloga149119sl