| Paper ID | SPTM-4.5 |
| Paper Title |
DECENTRALIZED OPTIMIZATION OVER NOISY, RATE-CONSTRAINED NETWORKS: HOW WE AGREE BY TALKING ABOUT HOW WE DISAGREE |
| Authors |
Rajarshi Saha, Stanford University, United States; Stefano Rini, National Chiao Tung University, Taiwan; Milind Rao, Amazon Alexa, United States; Andrea Goldsmith, Princeton University, United States |
| Session | SPTM-4: Estimation, Detection and Learning over Networks 2 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: Signal Processing over Networks |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In decentralized optimization, multiple nodes in a network collaborate to minimize the sum of their local loss functions. The information exchange between nodes required for the task is often limited by network connectivity. We consider a generalization of this setting, in which communication is further hindered by (i) a finite data-rate constraint on the signal transmitted by any node, and (ii) an additive noise corrupting the signal received by any node. We develop a novel algorithm for this scenario: Decentralized Lazy Mirror Descent with Differential Exchanges (DLMD-DiffEx), which guarantees convergence of the local estimates to the optimal solution under the given communication constraints. A salient feature of DLMD-DiffEx is the introduction of additional proxy variables that are maintained by the nodes to account for the disagreement in their estimates due to channel noise and data-rate constraints. We investigate the performance of DLMD-DiffEx both from a theoretical perspective as well as through numerical evaluations. |
