Blockchain as a Service: An Autonomous, Privacy Preserving, Decentralized Architecture for Deep Learning
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Deep learning algorithms have recently gained attention due to their inherent capabilities and the application opportunities that they provide. Two of the main reasons for the success of deep learning methods are the availability of processing power and big data. Both of these two are expensive and rare commodities that present limitations to the usage and implementation of deep learning. Decentralization of the processing and data is one of the most prevalent solutions for these issues. This paper proposes a cooperative decentralized deep learning architecture. The contributors can train deep learning models with private data and share them to the cooperative data-driven applications initiated elsewhere. Shared models are fused together to obtain a better model. In this work, the contributors can both design their own models or train the models provided by the initiator. In order to utilize an efficient decentralized learning algorithm, blockchain technology is incorporated as a method of creating an incentive-compatible market. In the proposed method, Ethereum blockchain's scripting capabilities are employed to devise a decentralized deep learning mechanism, which provides much higher, collective processing power and grants access to large amounts of data, which would be otherwise inaccessible. The technical description of the mechanism is described and the simulation results are presented.
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