Engineering Networked Machine Learning via Meta-Free Energy Minimisation

Inspired by neuroscience, informed by information-theoretic principles, and motivated by modern wireless systems architectures integrating artificial intelligence (AI) and communications, this Fellowship sets out to develop a paradigm-shifting framework for networked machine learning (ML) that is centred on free energy minimisation, networked meta-learning, and native integration of wireless communication and learning.