A Federated Mining Framework for Complete Frequent Itemsets.

In this paper, we address the common features of horizontal federated learning in data mining and propose a federated mining framework, which adopts a client-server model that cooperates with multiple data-source clients. The proposed algorithm handles client-side mining and server-side aggregation. For client-side mining, the algorithm uses prelarge itemsets to collect additional information for the server to integrate the clients' local mining results. For server-side aggregation, the algorithm considers the characteristics of large and prelarge itemsets sent from the clients and use a boundary strategy for integration. Experiments show that our method acquires the complete mined results while protecting data.