Name
Abstract | ||
|---|---|---|
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 gossip gradient descent, and establish epsilon-differential privacy and O(root log(2)t/epsilon(1-lambda(2))Nt) convergence, where lambda(2) is the second largest eigenvalue of the expected gossip matrix corresponding to the communication graph. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CDC.2018.8619437 | 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC) |
Field | DocType | ISSN |
Convergence (routing),Discrete mathematics,Mathematical optimization,Gradient descent,Computer science,Matrix (mathematics),Gossip,Distributed algorithm,Eigenvalues and eigenvectors,Linear regression,Lambda | Conference | 0743-1546 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Liu | 1 | 32 | 11.55 |
| Ji Liu | 2 | 146 | 26.61 |
| Tamer Basar | 3 | 3497 | 402.11 |