# Decentralized Federated Learning of Probabilistic Generative Classifiers

arXiv: https://arxiv.org/abs/2507.17285 · HTML: https://mlsanity.com/paper/2507.17285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [Generative Models for Effective ML on Private, Decentralized Datasets](https://mlsanity.com/paper/1911.06679/) (2019)
- [Decentralized Federated Learning through Proxy Model Sharing](https://mlsanity.com/paper/2111.11343/) (2021)
- [Classification of datasets with imputed missing values: does imputation quality matter?](https://mlsanity.com/paper/2206.08478/) (2022)
- [Probabilistic Imputation for Time-series Classification with Missing Data](https://mlsanity.com/paper/2308.06738/) (2023)
- [Federated Learning for Data Streams](https://mlsanity.com/paper/2301.01542/) (2023)
- [Partitioned Variational Inference: A Framework for Probabilistic Federated Learning](https://mlsanity.com/paper/2202.12275/) (2022)
- [Decentralized Bayesian Learning over Graphs](https://mlsanity.com/paper/1905.10466/) (2019)
- [Learning Vine Copula Models For Synthetic Data Generation](https://mlsanity.com/paper/1812.01226/) (2018)
- [pfl-research: simulation framework for accelerating research in Private Federated Learning](https://mlsanity.com/paper/2404.06430/) (2024)
- [Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach](https://mlsanity.com/paper/2401.08351/) (2024)
- [Risk-based Calibration for Generative Classifiers](https://mlsanity.com/paper/2409.03542/) (2024)
- [Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning](https://mlsanity.com/paper/2403.08810/) (2024)

## Abstract (arXiv)

Federated learning is a paradigm of increasing relevance in real world applications, aimed at building a global model across a network of heterogeneous users without requiring the sharing of private data. We focus on model learning over decentralized architectures, where users collaborate directly to update the global model without relying on a central server. In this context, the current paper proposes a novel approach to collaboratively learn probabilistic generative classifiers with a parametric form. The framework is composed by a communication network over a set of local nodes, each of one having its own local data, and a local updating rule. The proposal involves sharing local statistics with neighboring nodes, where each node aggregates the neighbors' information and iteratively learns its own local classifier, which progressively converges to a global model. Extensive experiments demonstrate that the algorithm consistently converges to a globally competitive model across a wide range of network topologies, network sizes, local dataset sizes, and extreme non-i.i.d. data distributions.