Research Interests

Aggregating a massive amount of data from heterogeneous devices, a distributed learning framework called Federated Learning(FL) is employed. FL is an emerging field of research in recent years. Some of our research directions include:

• Analysis of mobility-based COVID-19 epidemic model using Federated Multitask Learning

• Robust Online Federated Multitask Learning with the adaptive loss function

• Incentive mechanism of edge devices in Federated  Multitask Learning - A Stackelberg game approach

The timeliness of status message delivery in communications networks is subjective to time-varying wireless channel transmissions. Status update systems mainly rely on the freshness of data source information received at the remote destination. We therefore, are interested in: 

• Age of Information in multi-source system

• Age of information and federated learning