TY - GEN
T1 - Private Optimization on Networks
AU - Gade, Shripad
AU - Vaidya, Nitin H.
N1 - Funding Information:
1 (gade3@illinois.edu) and (nhv@illinois.edu) are with ECE Department, and Coordinated Science Laboratory, at University of Illinois Urbana-Champaign. This research is supported in part by NSF awards 1421918 and 1610543, and Toyota InfoTechnology Center. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funding agencies or the U.S. government.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - This paper considers a distributed multi-agent optimization problem, with the global objective consisting of the sum of local objective functions of the agents. The agents solve the optimization problem using local computation and communication between adjacent agents in the network. We present two randomized iterative algorithms for distributed optimization. To improve privacy, our algorithms add 'structured' randomization to the information exchanged between the agents. We prove deterministic correctness (in every execution) of the proposed algorithms despite the information being perturbed by noise with non-zero mean. We prove that a special case of a proposed algorithm (called function sharing) preserves privacy of individual polynomial objective functions under a suitable connectivity condition on the network topology.
AB - This paper considers a distributed multi-agent optimization problem, with the global objective consisting of the sum of local objective functions of the agents. The agents solve the optimization problem using local computation and communication between adjacent agents in the network. We present two randomized iterative algorithms for distributed optimization. To improve privacy, our algorithms add 'structured' randomization to the information exchanged between the agents. We prove deterministic correctness (in every execution) of the proposed algorithms despite the information being perturbed by noise with non-zero mean. We prove that a special case of a proposed algorithm (called function sharing) preserves privacy of individual polynomial objective functions under a suitable connectivity condition on the network topology.
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U2 - 10.23919/ACC.2018.8430960
DO - 10.23919/ACC.2018.8430960
M3 - Conference contribution
AN - SCOPUS:85052594255
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 1402
EP - 1409
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -