Structure Learning of Sparse GGMs over Multiple Access Networks
This addresses the challenge of distributed structure learning in resource-constrained networks, but it is incremental as it builds on existing GGM methods with new communication strategies.
The paper tackles the problem of learning sparse Gaussian Graphical Model structures from distributed datasets over bandwidth-limited wireless channels, proposing two communication strategies (Signs and Uncoded) and showing that both can recover the structure with high probability for large sample sizes, with experiments favoring the Signs method.
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets 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 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 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 large enough sample size. Experimental results indicate the superiority of Signs method over Uncoded method under several circumstances.