Core Concepts
The authors introduce a comprehensive framework for analyzing distributed algorithms, proving convergence to a common estimate. They develop specific algorithms within this framework to address dynamic convex optimization problems.
Abstract
The content discusses distributed fixed-point algorithms for dynamic convex optimization over decentralized wireless networks. It introduces a new OTA-C protocol for consensus in large networks, demonstrating low-latency and energy efficiency. The paper presents theoretical analysis, proofs of convergence, and practical applications in distributed supervised learning.
Stats
Numerous solvers developed in the past feature adaptations of local processing and consensus steps.
Superiorization is an efficient method for constrained optimization problems.
OTA-C is a scalable solution for distributed function computation over wireless networks.
The proposed algorithm demonstrates effectiveness in distributed supervised learning over time-varying wireless networks.