6 citations · 15 across the 9 of their papers we have counts for
9 papers
Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach
Han Wang, Leonardo F. Toso, Aritra Mitra +1
We study a model-free federated linear quadratic regulator (LQR) problem where M agents with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to minim…
Protection Against Graph-Based False Data Injection Attacks on Power Systems
Gal Morgenstern, Jip Kim, James Anderson +2
Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. Recent research has focused on developing GSP-ba…
Learning Personalized Models with Clustered System Identification
Leonardo F. Toso, Han Wang, James Anderson
We address the problem of learning linear system models from observing multiple trajectories from different system dynamics. This framework encompasses a collaborative scenario whe…
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
Han Wang, Aritra Mitra, Hamed Hassani +2
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting wi…
FedSysID: A Federated Approach to Sample-Efficient System Identification
Han Wang, Leonardo F. Toso, James Anderson
We study the problem of learning a linear system model from the observations of clients. The catch: Each client is observing data from a different dynamical system. This work a…
Mitigation-Aware Bidding Strategies in Electricity Markets
Yiqian Wu, Jip Kim, James Anderson
Market power exercise in the electricity markets distorts market prices and diminishes social welfare. Many markets have implemented market power mitigation processes to eliminate…
Identification of Intraday False Data Injection Attack on DER Dispatch Signals
Jip Kim, Siddharth Bhela, James Anderson +1
The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advan…
Distributionally Robust Decision Making Leveraging Conditional Distributions
Yuxiao Chen, Jip Kim, James Anderson
Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data.…
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation
Han Wang, Siddartha Marella, James Anderson
Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is pa…