In the era of rapid development in artificial intelligence and machine learning, data quality and relevancy are essential to generate usable applications of high quality and accuracy for machine learning.
As compared to publicly-accessible data, privately-held data are more relevant and timely for machine learning. These private data are usually untapped and inaccessible as they are stored in individual electronic devices such as smartphones, tablets and computers. Leading technology firms try to access these private data when individuals are unaware of or by providing free service to them in return. Nevertheless, these leading technology firms can only obtain a portion of the private data, which are subset of the massive untapped private data owned by all individuals.
Decentralized Machine Learning (DML) protocol is designed to expand the reach to untapped private data and unleash their potential to facilitate machine learning development while providing economic incentives and protecting data privacy. Machine learning algorithm will be run on the devices without extracting the data from the devices, which will be kept within the devices. Only the machine learning result will be aggregated with outcomes generated from other devices to form an unbiased, comprehensive and accurate crowdsourced analytics and predictions. Through DML protocol, both the private data and processing power for machine learning are decentralized as algorithms are run directly on individual devices by utilizing their idle processing power.
Decentralized Machine Learning
