Effective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the results, it can be seen that the designed model can help and improve the decision boundary by reducing the global loss function while maintaining both security and privacy.

有效的向量表示已被证明对信息物理系统中的事务分类和聚类任务很有用。传统方法使用基于启发式的方法和不同的修剪策略来有效地发现所需的模式。随着网络物理系统中事务数据的广泛和高维可用性，使用频繁项集（FI）作为特征的传统方法存在维数、稀疏性和隐私问题。在本文中，我们首先为交易分类任务提出了一个基于联邦学习的嵌入模型。该模型将交易数据作为一组频繁项集。之后，该模型可以通过保留频繁项集的上下文关系来学习低维连续向量。我们对高维交易数据的数量进行了深入的实验分析，以验证基于注意力机制和联邦学习的开发模型。从结果可以看出，设计的模型可以通过减少全局损失函数来帮助和改善决策边界，同时保持安全性和隐私性。