An Adaptive Federated Learning Framework for Knowledge Discovery from Multi-Source Isolated Data

This project aims to develop an adaptive FL framework for knowledge discovery from multi-source isolated data even in the presence of changing data distributions over time, while ensuring data privacy is preserved. The proposed research has applications in various domains, including improved services and the development of policies in healthcare, as well as increased collaboration among government agencies.