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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. 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