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Differentially Private Accelerated Distributed Algorithm for Aggregative Optimization
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Summary
Researchers propose a differentially private accelerated distributed gradient tracking algorithm for distributed aggregative optimization, where each agent's objective depends on an aggregate of all agents' decisions. The method perturbs exchanged information with independent Laplace noise, adds a noise deduction mechanism to keep estimates accurate, and combines dynamic average consensus with heavy-ball momentum. Under strong convexity and Lipschitz-continuous gradients, the authors prove linear convergence in mean-square error, derive explicit suboptimality bounds, and establish that the algorithm satisfies epsilon-DP; numerical simulations are provided.