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Structure learning of sparse GGMS over multiple access networks

Tavassolipour, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TCOMM.2019.2957080
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from a dataset distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designing effective strategies where reliable learning is feasible under power and bandwidth limitations. Two approaches are proposed: Signs and Uncoded methods. In the Signs method, the local machines quantize their data into binary vectors and an optimal channel coding scheme is used to reliably send the vectors to the central machine where the structure is learned from the received data. In the Uncoded method, data symbols are scaled and transmitted through the channel. The central machine uses the received noisy symbols to recover the structure. Theoretical results show that both methods can recover the structure with high probability for a large enough sample size. Experimental results indicate the superiority of the Signs method over the Uncoded method under several circumstances. © 2019 IEEE
  6. Keywords:
  7. Gaussian graphical model ; Structure learning ; Graphic methods ; Bandwidth limitation ; Distributed learning ; Gaussian graphical models ; High probability ; Multiple access channels ; Multiple access network ; Optimal channels ; Structure-learning ; Channel coding
  8. Source: IEEE Transactions on Communications ; Volume 68, Issue 2 , 2020 , Pages 987-997
  9. URL: https://ieeexplore.ieee.org/document/8918445