# Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference

arXiv: https://arxiv.org/abs/2607.20124 · HTML: https://mlsanity.com/paper/2607.20124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [The Convolutional Tsetlin Machine](https://mlsanity.com/paper/1905.09688/) (2019)
- [Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks](https://mlsanity.com/paper/2302.05675/) (2023)
- [Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier](https://mlsanity.com/paper/2011.08755/) (2020)
- [Tsetlin Machine Embedding: Representing Words Using Logical Expressions](https://mlsanity.com/paper/2301.00709/) (2023)
- [Low-Power Audio Keyword Spotting using Tsetlin Machines](https://mlsanity.com/paper/2101.11336/) (2021)
- [The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems](https://mlsanity.com/paper/1905.04206/) (2019)
- [TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines](https://mlsanity.com/paper/2309.04801/) (2023)
- [Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2502.08160/) (2025)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantification](https://mlsanity.com/paper/2410.17851/) (2024)
- [The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs](https://mlsanity.com/paper/2507.14874/) (2025)
- [TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering](https://mlsanity.com/paper/2409.10392/) (2024)

## Abstract (arXiv)

Tsetlin Machine (TM) is a rule-based machine-learning algorithm comprising collectives of two-action Tsetlin Automata (TAs) that cooperatively form conjunctive logical clauses from Boolean inputs through stochastic feedback. Although few recent studies have examined TM Federated Learning, the broader area of distributed and decentralized TM learning has not received much attention in the existing literature and warrants further exploration. In this work, we propose a paradigm for decentralized collaborative learning under a vertical feature-partitioning setting among an ensemble of Tsetlin Machines using consensus-based inference. Within this decentralized paradigm, each agent maintains its own private TM model, and there is no exchange of raw data among agents. Inference combines individual agents model predictions into a global consensus. The paradigm accommodates heterogeneous TM-based agents with differing data acquisition means, local data distributions, or computational resources, thereby facilitating the integration and fusion of information in settings such as multi-modal sensing environments. Experiments conducted using two-dimensional grid and connected graph network topologies demonstrate that the classification accuracies achieved are comparable to those of centralized models.