Filtering out High Noise Data for Distributed Deep Neural Networks

Artificial intelligence-based cyber-physical systems (CPS) applications have been spread across various fields such as smart cities, medical services, and industrial controls. When CPS devices are connected to a cloud server, big data streams generated by CPS devices impose enormous bandwidth pressure and exert excessive compute loads to the cloud server. Due to unpredictable environments and uncertainty in reality, these issues are mainly attributed to a large amount of high noise data captured and uploaded by CPS devices. To overcome these issues, this paper proposes a cyber-physical-cloud based framework for distributed deep neural networks (DDNNs) to prevent high noise data from being uploaded to the cloud. The proposed framework features a lightweight data filtering module enabled by depthwise separable convolutions to identify and filter out the high noise data that the cloud cannot recognize. Extensive experimental results demonstrate that the proposed data filtering module can achieve an accuracy of up to 83.72% in identifying high noise data and the proposed framework can effectively save bandwidth of up to 63.42% as compared to benchmarking methods. Note to Practitioners—This paper is motivated by the problems of enormous bandwidth pressure and excessive cloud compute loads in cyber-physical-cloud distributed computing paradigms. These problems are mainly caused by high noise data generated by CPS devices, because CPS devices often work in disturbing and unstable environments and there are uncontrollable uncertainties in reality. Especially for the emerging artificial intelligence-driven cyber-physical-cloud distributed paradigms, there is no existing research to solve the unnecessary transmission and cloud compute loads caused by high noise data. To tackle the challenge, this paper develops a novel cyber-physical-cloud distributed framework with data filtering capabilities to prevent high noise data from being uploaded. The proposed framework supports two popular loosely coupled and closely coupled distributed computing paradigms. Extensive experiments confirm that the proposed cyber-physical-cloud distributed framework can efficiently filter out high noise data and alleviate unnecessary transmission and needless cloud compute loads introduced by high noise data.