SWARM LAB

We are a group of scientists and engineers working at the intersection between robotics, control theory, machine learning, and game theory to design high performance, interactive autonomous systems. Our lab is based in the Department of Electrical and Computer Engineering Wireless Networking and Communications Group (WNCG) and Texas Robotics at UT Austin... Federated learning (FL) has been widely used in multi-agent systems with homogeneous neural models. However, FL is not suitable when agents have heterogeneous models whose gradients of weights cannot be shared. We proposed decentralized data-sharing algorithm for agents with heterogeneous models. We aim to design provably convergent algorithms achieving the same performance as the centralized ones with linear time complexity execution. We envision that related challenges are in the following aspects: 1) how to share data while preserving privacy; 2) how to evaluate the contribution of data shared to others in terms of model..