2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-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
SessionSPTM-4: Estimation, Detection and Learning over Networks 2
LocationGather.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
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.