Discovering frequent Itemsets is an interesting problem in the context of parallel and distributed databases. Computation cost and communication/synchronization overhead are important elements in distributed Frequent Itemsets. In this work, we propose an efficient algorithm for mining distributed frequent Itemsets (MDFI) which can significantly reduce the number of candidates Itemsets and communication costs by adopting a Master/Slaves scheme of communication. We present performance comparisons for our algorithm against Apriori and FP-growth algorithms using two datasets with different minimum support.