Differentially Private Distributed Convex Optimization. (arXiv:2302.14514v1 [math.OC])

This paper considers distributed optimization (DO) where multiple agents
cooperate to minimize a global objective function, expressed as a sum of local
objectives, subject to some constraints. In DO, each agent iteratively solves a
local optimization model constructed by its own data and communicates some
information (e.g., a local solution) with its neighbors until a global solution
is obtained. Even though locally stored data are not shared with other agents,
it is still possible to reconstruct the data from the …

agent constraints data distributed function global information local locally math objectives optimization own private solution