AINeutralarXiv – CS AI · 9h ago6/10
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Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting
Researchers propose a decentralized gradient descent framework for optimizing time-varying objectives across distributed networks processing streaming data. The work analyzes tracking error using temporal weighting strategies, showing uniform weighting achieves O(1/t) convergence while exponential discounting maintains non-vanishing error floors, with implications for distributed machine learning systems.