This thesis explores the use of advanced Variational Autoencoder (VAE) architectures to enhance privacy in Federated Learning. The goal is to design and evaluate methods that learn useful representations while minimizing the risk of sensitive data leakage. The approach will involve extending standard VAE models to better support privacy-preserving objectives in distributed environments. The student will investigate trade-offs between privacy and utility, and benchmark the approach against existing techniques. 

This thesis explores the concept of DNA as a medium for molecular computation, focusing on the design and implementation of fundamental computational circuits using DNA strands. By leveraging reactions of DNA molecules, such as strand displacement and transcription, the research demonstrates how logic gates, arithmetic operations, and basic control mechanisms can be encoded and executed at the molecular level. The work highlights the potential of DNA computing for genetic circuits, parallel processing and other applications.