1. An efficient parallel machine learning-based blockchain framework
- Author
-
Tzu-Chieh Tang, Yi-Ping Chen, Yu-Chen Luo, and Chun-Wei Tsai
- Subjects
Hyperparameter ,Blockchain ,Computer Networks and Communications ,Computer science ,business.industry ,Deep learning ,Limiting ,Information technology ,Critical research ,Machine learning ,computer.software_genre ,T58.5-58.64 ,Artificial Intelligence ,Hardware and Architecture ,Information system ,Artificial intelligence ,business ,computer ,Metaheuristic ,Software ,Information Systems - Abstract
The unlimited possibilities of machine learning have been shown in several successful reports and applications. However, how to make sure that the searched results of a machine learning system are not tampered by anyone and how to prevent the other users in the same network environment from easily getting our private data are two critical research issues when we immerse into powerful machine learning-based systems or applications. This situation is just like other modern information systems that confront security and privacy issues. The development of blockchain provides us an alternative way to address these two issues. That is why some recent studies have attempted to develop machine learning systems with blockchain technologies or to apply machine learning methods to blockchain systems. To show what the combination of blockchain and machine learning is capable of doing, in this paper, we proposed a parallel framework to find out suitable hyperparameters of deep learning in a blockchain environment by using a metaheuristic algorithm. The proposed framework also takes into account the issue of communication cost, by limiting the number of information exchanges between miners and blockchain.
- Published
- 2021