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

arXiv: https://arxiv.org/abs/2208.00522 · HTML: https://mlsanity.com/paper/2208.00522/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Online Continuous Submodular Maximization](https://mlsanity.com/paper/1802.06052/) (2018)
- [COLA: Decentralized Linear Learning](https://mlsanity.com/paper/1808.04883/) (2018)
- [Pruning Random Forests for Prediction on a Budget](https://mlsanity.com/paper/1606.05060/) (2016)
- [Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems](https://mlsanity.com/paper/1612.01216/) (2016)

## 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.