Variational inequalities are a broad and flexible class of problems that includes
minimization, saddle point, and fixed point problems as special cases. Therefore, variational …

Decentralized learning enables edge users to collaboratively train models by exchanging
information via device-to-device communication, yet prior works have been limited to …

Decentralized Stochastic Gradient Descent (D-SGD) represents an efficient communication
approach tailored for mastering insights from vast, distributed datasets. Inspired by parallel …

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 …

In many deep learning (DL) applications, the desire for ever higher accuracy and the new
ubiquity of transfer learning has led to a marked increase in the size and depth of model …