Federated Erasable-Itemset Mining with Quasi-Erasable Itemsets

Erasable-itemset mining plays a crucial role in the research field of manufacturing, especially in helping identify materials with lower profits in product datasets, providing important bases for managers to make wiser decisions. In today's information age, how to share data within a secure framework has become a significant issue. To address this challenge, this paper combines the concepts of federated learning and data mining, particularly in erasable-itemset mining, to propose a federated mining framework. The unique aspect of this framework is that it can effectively mine erasable itemsets from multiple dispersed datasets without the need for direct data sharing. This not only enhances processing efficiency but also protects the privacy of data owners. Our proposed algorithm covers two main steps: client-side mining and server-side data aggregation. Experiments show that our method, while ensuring data security, successfully obtains partial mining results, proving its practicality and effectiveness.