FEAS Professor’s research recognized among top 10 federated learning papers
Dr. Min Dong, Professor, Faculty of Engineering and Applied Science, has co-authored a paper recognized as one of the .
Federated learning (FL) is a distributed machine learning technique that allows multiple devices to learn a global model collaboratively using their local datasets without sharing their raw data to preserve data privacy.
Titled , the research addresses key challenges in FL over resource-constrained wireless systems. The study proposes optimized transceiver processing and device selection techniques to enhance wireless FL performance while maintaining data privacy.
