Due to lack of scalability of feature selection algorithms when applied in a centralized manner, most classification algorithms perform sub-optimally especially in the presence of irrelevant and redundant features in high dimensional datasets-large feature size small instances. Though it is imperative to remove insignificant features to improve learning, the process is complex and time-consuming.