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Multi-Task Diffusion With Masked Measurements

delete2025-01-01
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PRE
AI
M
Mahdi Shamsi
F
Farokh Marvasti
DOI：10.1109/LSP.2025.3600370delete
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Abstract

Abstract

En 中文
This letter addresses the problem of clustered multitask distributed estimation under masked measurements, where network nodes observe partial or incomplete data due to sensing limitations, communication constraints, or privacy requirements. We propose a novel extension of the Diffusion LMS (DLMS) algorithm that incorporates node-specific masking and a task-clustered structure. A tailored network-wide optimization problem is formulated to jointly handle masked observations and inter-cluster multitask estimation. Convergence analysis and simulation results demonstrate the effectiveness and robustness of the proposed approach in improving estimation performance under partial observability.
Keywords:
Diffusion LMS
distributed estimation
masked measurements
multitask diffusion
partial observation

Journal

I
IEEE Signal Processing Letters
IF:
3.9
Papers:
833
Citations:
0

Organization

S
Sharif University of Technology
Scholars:
1.1W
Papers: 1.1W
Citations: 9.5K
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