Abstract Personalized Federated Learning (PFL) aims to train machine learning models on
decentralized, heterogeneous data while preserving user privacy. This research survey …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Data heterogeneity across participating devices poses one of the main challenges in
federated learning as it has been shown to greatly hamper its convergence time and …

Determinantal Point Process (DPP) is a powerful technique to enhance data diversity by
promoting the repulsion of similar elements in the selected samples. Particularly, DPP …

In the realm of future 6G wireless networks, integrating the intelligent edge through the
advent of AI signifies a momentous leap forward, promising revolutionary advancements in …

Artificial intelligence is widely viewed as a key enabler of sixth generation wireless systems.
In this thesis we target fundamental problems arising from the interaction between these two …

Conventional federated learning (FL) frameworks follow a server-centric model where the
server determines session initiation and client participation. We introduce Client-Centric …