SCL: A sustainable deep learning solution for edge computing ecosystem in smart manufacturing

Edge computing empowered Deep Learning (DL) solutions have risen as the foremost facilitators of automation in a multitude of smart manufacturing applications. These models are implemented on edge devices with frozen learning capabilities to execute DL inference task(s). Nevertheless, the data they process is susceptible to intermittent alterations amidst the ever-changing landscape of dynamic smart manufacturing ecosystem. It sparks the demand for model maintenance solution(s) to address adaptability and dynamism issues to enhance the efficiency of smart manufacturing solutions. Moreover, additional issue(s), such as the non-availability of comprehensive data (or the availability of solely contemporary data), near-real-time execution of DL model maintenance task, etc., imposes daunting obstructions in devising an efficient DL model maintenance strategy. This work proposes a novel approach that encompasses the merits of Continual Learning (CL) and Split Learning (SL) driven by edge intelligence, amalgamating them into a hybrid solution aptly named Split-based Continual Learning (SCL). CL ensures the sustained performance of the DL model amidst constraints related to data availability. At the same time, SL empowers near-real-time execution at the edge to achieve improved efficiency. An extension of the SCL scheme, termed as Extended SCL (ESCL), is implemented to addresses the interaction soundness aspects among the mobile edge devices in a collaborative execution environment. Evaluation of a vision-based product-quality inspection use case in an emulated hardware test-bed setup signifies that the performance of SCL and ESCL schemes have the potential to meet the needs of smart manufacturing. SCL attains an appreciable reduction in the model maintenance cost in the range of 21 to 48 and 12 to 29 percent compared to the ECN-only and basic-SL schemes. The ESCL scheme further improved the performance by 18 to 34 and 20 to 36 percent respectively over the basic-SL and SCL.