Processing a federated query in Linked Data is challenging because it needs to consider the number of sources, the source locations as well as heterogeneous system such as hardware, software and data structure and distribution. In this work, we investigate the relationship between the data distribution and the communication cost in a federated SPARQL query framework. We introduce the spreading factor as a dataset metric for computing the distribution of classes and properties throughout a set of data sources. To observe the relationship between the spreading factor and the communication cost, we generate 9 datasets by using several data fragmentation and allocation strategies. Our experimental results showed that the spreading factor is correlated with the communication cost between a federated engine and the SPARQL endpoints. In terms of partitioning strategies, partitioning triples based on the properties and classes can minimize the communication cost. However, such partitioning can also reduce the performance of SPARQL endpoint within the federation framework.