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

Source

Source name: arXiv
Source kind: paper
Authors / organization: Yehuda Rudin, Osnat Keren, Michal Yemini, Alexander Fish
Venue: arXiv: cs.LG
Published: 2026-07-22
Collected: 2026-07-23
URL: http://arxiv.org/abs/2607.20124v1
PDF: https://arxiv.org/pdf/2607.20124v1
DOI: UNKNOWN

Plain-Language Brief

The researchers propose a decentralized collaborative learning paradigm for an ensemble of Tsetlin Machines (TMs) using consensus-based inference. Each agent has its own private TM model, and they combine individual predictions to create a global consensus. This approach works well in settings with heterogeneous agents and can be applied to multi-modal sensing environments.

What the Document Says

Tsetlin Machine is a machine learning algorithm consisting of collectives of two-action Tsetlin Automata that form conjunctive logical clauses from Boolean inputs.
Recent studies have examined TM Federated Learning, but distributed and decentralized TM learning has not been extensively studied.
The paper introduces a paradigm for decentralized collaborative learning among an ensemble of Tsetlin Machines in a vertical feature-partitioning setting using consensus-based inference.
In this approach, each agent maintains its own private TM model and there is no exchange of raw data between agents.
The paradigm accommodates heterogeneous TM-based agents with differing data acquisition means, local data distributions, or computational resources.
Experiments using grid and connected graph network topologies show that the classification accuracies achieved are comparable to centralized models.

Key Concepts Mentioned

Tsetlin Machine
Tsetlin Automata
Federated Learning
Distributed Learning
Decentralized Learning
Consensus-based Inference
Vertical Feature-Partitioning

Claims Made by the Document

The proposed paradigm can facilitate the integration and fusion of information in multi-modal sensing environments.
Classification accuracies achieved with this approach are comparable to centralized models.

Evidence Used

Experiments conducted using grid and connected graph network topologies

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.