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Local Differential Privacy in Federated Optimization. (arXiv:2304.01510v1 [cs.MA])
cs.CR updates on arXiv.org arxiv.org
Federated optimization, wherein several agents in a network collaborate with
a central server to achieve optimal social cost over the network with no
requirement for exchanging information among agents, has attracted significant
interest from the research community. In this context, agents demand resources
based on their local computation. Due to the exchange of optimization
parameters such as states, constraints, or objective functions with a central
server, an adversary may infer sensitive information of agents. We develop
LDP-AIMD, a local differentially-private …
adversary algorithm community computation constraints context cost demand differential privacy exchange functions information interest local may network optimization privacy research resources sensitive information server social states the exchange