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This paper is an extension of work originally presented in PM2.5 Prediction-based Weather Forecast Information and Missingness Challenges: A Case Study Industrial and Metropolis Areas, which focused on imputation algorithm to solve missingness challenge and demonstrated a basic prediction system to prove the proposed algorithm, II-MPA. Distributed and decentralized systems, recently, have been proven for their effectiveness in multiple perspectives. This paper introduces "Overmind", the solution that governs and builds the network of decentralized machine learning as a prediction framework named after its functionality: it aims to discover a set of data and associated attributes for assigning machine learnings in the collaborative decentralized manner. Overmind also empowers feature transfer learning with data preservation. It demonstrates how discovered features are transferred and shared among synergic agents in the network. This model is tested and evaluated the accuracy against the traditional single machine learning prediction model in the original work. The results are satisfactory in both prediction performance and transfer learning.

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