Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting
Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where data arrive sequentially and decisions must be updated continuously. In this work, we study optimization with streaming data over a distributed network of agents. We adopt a structured, weight-based formulation that explicitly captures the streaming-data origin of the time-varying objective: at each time step, every agent receives a new sample, and the network seeks to track the minimizer of a temporally weighted objective formed from all samples observed across the network so far. We focus on decentralized gradient descent (DGD) with a lim...
| Authors | Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson |
|---|---|
| Primary category | eess.SP |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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