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

2026-07-22

AI Summary

This work addresses the challenge of collaborative learning among multiple agents under vertically partitioned features, where raw data cannot be exchanged. It proposes a decentralized collaborative learning paradigm based on consensus reasoning, wherein each agent maintains a private Tsetlin Machine model and achieves global consensus through iterative fusion of local predictions—without sharing original data. This approach pioneers the integration of consensus mechanisms into Tsetlin Machine ensembles, enabling heterogeneous agents to collaboratively learn in distributed, multimodal environments and substantially expanding the applicability of Tsetlin Machines. Experimental results demonstrate that, under both two-dimensional grid and connected graph topologies, the method attains classification accuracy comparable to that of centralized models.

Abstract

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.

Problem

• Decentralized learning
• Tsetlin Machine
• Vertical feature partitioning
• Collaborative learning
• Consensus-based inference

Innovation

• Tsetlin Machine
• decentralized learning
• consensus-based inference
• vertical feature partitioning
• federated learning