# Dictionary Learning over Distributed Models

arXiv: https://arxiv.org/abs/1402.1515 · HTML: https://mlsanity.com/paper/1402.1515/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2014.

## Built on by (most cited first, 8 of 8)

- [Stability and Generalization for Markov Chain Stochastic Gradient Methods](https://mlsanity.com/paper/2209.08005/) (2022)
- [GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning](https://mlsanity.com/paper/2105.01231/) (2021)
- [PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities](https://mlsanity.com/paper/2303.02532/) (2023)
- [Continual Learning with Distributed Optimization: Does CoCoA Forget?](https://mlsanity.com/paper/2211.16994/) (2022)
- [Decentralized Weakly Convex Optimization Over the Stiefel Manifold](https://mlsanity.com/paper/2303.17779/) (2023)
- [Decentralized Complete Dictionary Learning via ℓ4-Norm Maximization](https://mlsanity.com/paper/2211.03628/) (2022)
- [Distributed Continual Learning with CoCoA in High-dimensional Linear Regression](https://mlsanity.com/paper/2312.01795/) (2023)
- [Stability and Generalization for Decentralized Markov SGD](https://mlsanity.com/paper/2605.01701/) (2026)

## Abstract (arXiv)

In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread over different spatial locations and it is not feasible to aggregate all dictionaries in one location due to communication and privacy considerations. We first show that the dual function of the inference problem is an aggregation of individual cost functions associated with different agents, which can then be minimized efficiently by means of diffusion strategies. The collaborative inference step generates dual variables that are used by the agents to update their dictionaries without the need to share these dictionaries or even the coefficient models for the training data. This is a powerful property that leads to an effective distributed procedure for learning dictionaries over large networks (e.g., hundreds of agents in our experiments). Furthermore, the proposed learning strategy operates in an online manner and is able to respond to streaming data, where each data sample is presented to the network once.