A Dynamical System Model of Federated Learning
A. Agarwal, G. Joshi, and L. Pileggi, “FedECADO: A Dynamical System Model of Federated Learning,” The International Conference on Machine Learning (ICML), July 2025.
A. Agarwal, G. Joshi, and L. Pileggi, “FedECADO: A Dynamical System Model of Federated Learning,” The International Conference on Machine Learning (ICML), July 2025.
D. Garg and L. Pileggi, Universal Topological Arrays: An Efficient Solution for Provably Secure Hardware, IEEE Computer Society Annual Symposium on VLSI (ISVLSI), July 6-9, 2025.
Shimiao Li, Amritanshu Pandey, and Larry Pileggi. “Contingency Analysis with Warm Starter using Probabilistic Graphical Model.” Power Systems Computation Conference (PSCC), June 2024.
C. Talbot, Deepali Garg, L. Pileggi and K. Mai, “An IP-Agnostic Foundational Cell Array Offering Supply Chain Security,” The 61st Design Automation Conference, June 2024.
C. Talbot, Deepali Garg, L. Pileggi and K. Mai, “IP-Agnostic Standard Cell Fabric Offering Tamper Resistance and Supply Chain Resilience,” Government Microcircuit Applications and Critical Technology Conference (GOMACTech), March 2024.
D. Garg, J. Sweeney and L. Pileggi, Quantifying the Efficacy of Logic Locking Methods, International Conference on VLSI Design, Kolkata India, January 2024.
S. Li, J. Drgona, S. Abhyankar, L. Pileggi, Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning, 5th International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES 2023), Orlando FL, June 2023.
E. Foster, T. McNamara, A. Pandey and L. Pileggi, Actionable Three-Phase Infeasibility Optimization with Varying Slack Sources, IEEE PES General Meeting, July 16-20, 2023
B. Singer, A. Pandey, S. Li, L. Bauer, C. Miller, L. Pileggi, V. Sekar, Shedding Light on Inconsistencies in Grid Cybersecurity: Disconnects and Recommendations, IEEE Symposium on Security and Privacy, May 22-26, 2023.
P. Donti, A. Agarwal, L. Pileggi, Z. Kolter, Adversarially Robust Learning for Security-Constrained Optimal Power Flow, Neural Information Processing Systems, 2021
Carnegie Mellon University
Hamerschlag Hall, 2113
5000 Forbes Avenue
Pittsburgh, PA 15213-3891 USA
pileggi@andrew.cmu.edu
Phone: 412-268-6774