Training Machine Learning Models Through Preserved Decentralization

Abstract

In the era of big data, fast and effective machine learning algorithms are urgently required for large-scale data analysis. Data is usually created from several parts and stored in a geographically distributed manner, which has stimulated research in the field of distributed machine learning. The traditional master-level distributed learning algorithm involves the use of a trusted central server and focuses on the online privacy model. On the contrary, the specific linear learning model and security issues are not well understood in this column. We built a decentralized advanced-Proof-of-Work (aPoW) algorithm specifically for learning a general predictive model over the blockchain. In aPoW, we establish the data privacy of the differential privacy based schemes to protect each party and propose a secure domain against potential Byzantine attacks at a reduced rate. We explored a technical module in newsprint to consider a universal learning model (linear or non-linear) to provide a secure, confidential decentralized machine learning system called deepLearning Chain. Finally, we introduce deepLearning Chain on blockchain through comprehensive experiments, demonstrate its performance and effectiveness.