DDLPF: A Practical Decentralized Deep Learning Paradigm for Internet-of-Things Applications

In recent years, it has been observed the exponential growth of the Internet of Things (IoT) in different application fields, such as manufacturing and energy industry. To effectively fuse and process the tremendous amount of IoT sensing data timely, there is an urgent need to shift from a conventional centralized computing to a decentralized computing. However, there remain some essential technical challenges to develop effective decentralized computing methods in the context of IoT applications, including 1) the timely response, sufficient privacy preservation, and high security are normally required in IoT-related applications and 2) the biases and non-independent identically distributed (IID) properties potentially presented in the IoT sensing data. To address these challenges, in this article, we propose a decentralized deep learning paradigm with privacy-preservation and fast few-shot learning (DDLPF) by exploiting federated learning, metalearning, and blockchain techniques. In the simulation section, we evaluate the performance of our proposed DDLPF paradigm in different scenarios and compare it with other existing techniques.