# Distributed Learning for Cooperative Inference

arXiv: https://arxiv.org/abs/1704.02718 · HTML: https://mlsanity.com/paper/1704.02718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Communication-efficient Distributed Cooperative Learning with Compressed Beliefs](https://mlsanity.com/paper/2102.07767/) (2021)
- [An Asynchronous Decentralized Algorithm for Wasserstein Barycenter Problem](https://mlsanity.com/paper/2304.11653/) (2023)

## Abstract (arXiv)

We study the problem of cooperative inference where a group of agents interact over a network and seek to estimate a joint parameter that best explains a set of observations. Agents do not know the network topology or the observations of other agents. We explore a variational interpretation of the Bayesian posterior density, and its relation to the stochastic mirror descent algorithm, to propose a new distributed learning algorithm. We show that, under appropriate assumptions, the beliefs generated by the proposed algorithm concentrate around the true parameter exponentially fast. We provide explicit non-asymptotic bounds for the convergence rate. Moreover, we develop explicit and computationally efficient algorithms for observation models belonging to exponential families.