Distributed Interpretable Machine Learning on GPUs

Activity: Talk or presentationConference presentation


The research introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM) to address large-scale regularized Sparse Machine Learning (SML) problems over a network of computational nodes. These problems involve minimizing local convex loss functions over a global decision vector with an explicit $\ell_0$ norm constraint to ensure sparsity. This approach generalizes various sparse regression and classification models, including sparse linear and logistic regression, sparse softmax regression, and sparse support vector machines. Bi-cADMM reformulates the original non-convex SML problem using bi-linear consensus and employs a hierarchical decomposition strategy. This strategy splits the problem into smaller, parallel-computable sub-problems through a two-phase approach: initial sample decomposition and distribution of local datasets across nodes, followed by a delayed feature decomposition on GPUs available to each node. GPUs handle data-intensive computations, while CPUs manage less demanding tasks. The algorithm is implemented in an open-source Python package called Parallel Sparse Fitting Toolbox (PsFiT). Computational experiments validate the efficiency and scalability of Bi-cADMM through numerical benchmarks on various SML problems with distributed datasets.
Period30 Jun 20244 Jul 2024
Event title33rd European Conference on Operational Research
Event typeConference
LocationCopenhagen, DenmarkShow on map
Degree of RecognitionInternational


  • Federated Learning
  • Distributed Optimization
  • GPU Computing