This project will explore Federated Learning (FL) and its potential to address data privacy issues. It will investigate innovative FL architecture and protocols to handle heterogeneity in data modalities and model architectures, analyzing the trade-off between communication costs and global performance. Additionally, it will tackle different computation capabilities to ensure fairness across different local resources when simultaneously dealing with uni- and multimodal clients.
