mlsanityData to 7 Oct 2026

Online Decentralized Frank-Wolfe: From theoretical bound to applications in smart-building

arXiv 2208.00522 ↗First posted 31 Jul 2022cs.LG cs.DS math.OCcited by 0

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Abstract (arXiv)

The design of decentralized learning algorithms is important in the fast-growing world in which data are distributed over participants with limited local computation resources and communication. In this direction, we propose an online algorithm minimizing non-convex loss functions aggregated from individual data/models distributed over a network. We provide the theoretical performance guarantee of our algorithm and demonstrate its utility on a real life smart building.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 31 Jul 2022.

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7 of 27 references.

References · 7

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2019Introduction to Online Convex Optimizationcited by 793
  3. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  4. 2018Online Continuous Submodular Maximizationcited by 35
  5. 2018COLA: Decentralized Linear Learningcited by 30
  6. 2016Pruning Random Forests for Prediction on a Budgetcited by 14
  7. 2016Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problemscited by 11

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