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Distributed Personalized Gradient Tracking With Convex Parametric Models
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DOI：10.1109/TAC.2022.3147007.png)
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Abstract
En 中文
We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (user-specific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure a priori, whose parameters are to be learned along with the evolution of the algorithm. The algorithm is composed of two intertwined components: 1) a dynamic gradient tracking scheme for finding local solution estimates and 2) a recursive least squares scheme for estimating the unknown parameters via user's noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis.
Keywords:
Distributed learning
distributed optimization
online optimization
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W