发表机构
Faculty of Engineering, Bar Ilan University（巴伊兰大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对分布式和去中心化TM学习关注不足的问题，提出基于共识推理的Tsetlin机集成去中心化协作学习范式，各智能体维护私有模型，不交换原始数据，实验表明该范式下分类准确率与集中式模型相当，促进了信息集成融合。
AI 中文摘要
Tsetlin机（TM）是一种基于规则的机器学习算法，由双动作Tsetlin自动机（TA）组成，通过随机反馈从布尔输入中协作形成合取逻辑子句。近期虽有研究探讨TM联邦学习，但分布式和去中心化TM学习在现有文献中未受太多关注，值得进一步探索。本文提出一种在垂直特征划分设置下，Tsetlin机集成中基于共识推理的去中心化协作学习范式。在此范式中，各智能体维护自己的私有TM模型，智能体间不交换原始数据，推理将个体模型预测整合为全局共识。该范式适用于具有不同数据获取方式、局部数据分布或计算资源的基于TM的异构智能体，促进了多模态传感环境等场景下信息的集成与融合。使用二维网格和连通图网络拓扑进行的实验表明，所实现的分类准确率与集中式模型相当。
英文摘要
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.
