Federated Learning (FL) in Machine Learning (ML) has become very popular nowadays because it trains classifiers across multiple decentralized devices without transferring data to a central server. It is a decentralized learning approach that amalgamates several different nodes into one. In 2016, Google introduced the concept of FL when the use and misuse of personal data were gaining global attention. In this paper, we have proposed a privacy-preserving approach for mining big personalized data employing scalable decision tree induction with FL. The concept of the RainForest framework refers to addressing big data challenges via a Decision Tree (DT) classifier. The proposed method does not share the data or personal information with the central server. It only transfers the local models' parameter values to the central server. Each individual device or node trains its own local DT classifier and shares the prior and conditional probability values with the central server. We have tried to simulate the proposed concept using five benchmark datasets. The results of the evaluation indicate that the model exhibits exceptional performance and accuracy.

MOTIVATION: Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available. RESULTS: https://github.com/mrzResearchArena/PyFeat/blob/master/RESULTS.md. AVAILABILITY AND IMPLEMENTATION: Toolkit, source code and manual to use PyFeat: https://github.com/mrzResearchArena/PyFeat/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.