This $209,988 Project Grant award from the National Science Foundation (NSF) Integrative Activities (CFDA 47.083) program supports research at Clemson University to establish theoretical and algorithmic foundations for ensuring differential privacy in decentralized optimization algorithms without losing provable optimality.
The key research thrusts include:
- Investigating the tradeoff between convergence speed and differential privacy in decentralized optimization,
- Exploring differential privacy in decentralized online optimization,
- Establishing differential privacy under shared coupling constraints in decentralized optimization,
- Exploring differential privacy in decentralized Nash games without losing provable optimality,
- Evaluating the results through numerical simulations and real-world experiments in smart grids and networked intelligent vehicles.
This award aims to broadly enable more effective privacy protections for decentralized networks and impact education by enhancing the curriculum on control and networked systems, as well as training students in interdisciplinary information privacy research.
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