TY - GEN

T1 - Differentially Private Gossip Gradient Descent

AU - Liu, Yang

AU - Liu, Ji

AU - Basar, Tamer

N1 - Publisher Copyright:
© 2018 IEEE.

PY - 2018/7/2

Y1 - 2018/7/2

N2 - In this paper, we study a problem of learning a linear regression model distributively with a network of N interconnected agents in which each agent can deploy an online learning algorithm to adaptively learn the regression model using its private data. The goal of the problem is to devise a distributed algorithm, under the constraint that each agent can communicate only with its neighbors depicted by a connected communication graph, which enables all N agents converge to the true model, with a performance comparable to that of conventional centralized algorithms. We propose a differentially private distributed algorithm, called private gossi» gradient descent, and establish E-differential privacy and Oleft(sqrt{frac{log {2}t}{epsilon(1-lambda-{2})Nt}}right) convergence, where A2 is the second largest eigenvalue of the expected gossip matrix corresponding to the communication graph.

AB - In this paper, we study a problem of learning a linear regression model distributively with a network of N interconnected agents in which each agent can deploy an online learning algorithm to adaptively learn the regression model using its private data. The goal of the problem is to devise a distributed algorithm, under the constraint that each agent can communicate only with its neighbors depicted by a connected communication graph, which enables all N agents converge to the true model, with a performance comparable to that of conventional centralized algorithms. We propose a differentially private distributed algorithm, called private gossi» gradient descent, and establish E-differential privacy and Oleft(sqrt{frac{log {2}t}{epsilon(1-lambda-{2})Nt}}right) convergence, where A2 is the second largest eigenvalue of the expected gossip matrix corresponding to the communication graph.

UR - http://www.scopus.com/inward/record.url?scp=85062193605&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062193605&partnerID=8YFLogxK

U2 - 10.1109/CDC.2018.8619437

DO - 10.1109/CDC.2018.8619437

M3 - Conference contribution

AN - SCOPUS:85062193605

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 2777

EP - 2782

BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 57th IEEE Conference on Decision and Control, CDC 2018

Y2 - 17 December 2018 through 19 December 2018

ER -