Digital twins (DTs) are revolutionizing the biotechnology industry by
enabling sophisticated digital representations of biological assets,
microorganisms, drug development processes, and digital health applications.
However, digital twinning at micro and nano scales, particularly in modeling
complex entities like bacteria, presents significant challenges in terms of
requiring advanced Internet of Things (IoT) infrastructure and computing
approaches to achieve enhanced accuracy and scalability. In this work, we
propose a novel framework that integrates the Internet of Bio-Nano Things
(IoBNT) with advanced machine learning techniques, specifically convolutional
neural networks (CNN) and federated learning (FL), to effectively tackle the
identified challenges. Within our framework, IoBNT devices are deployed to
gather image-based biological data across various physical environments,
leveraging the strong capabilities of CNNs for robust machine vision and
pattern recognition. Subsequently, FL is utilized to aggregate insights from
these disparate data sources, creating a refined global model that continually
enhances accuracy and predictive reliability, which is crucial for the
effective deployment of DTs in biotechnology. The primary contribution is the
development of a novel framework that synergistically combines CNN and FL,
augmented by the capabilities of the IoBNT. This novel approach is specifically
tailored to enhancing DTs in the biotechnology industry. The results showcase
enhancements in the reliability and safety of microorganism DTs, while
preserving their accuracy. Furthermore, the proposed framework excels in energy
efficiency and security, offering a user-friendly and adaptable solution. This
broadens its applicability across diverse sectors, including biotechnology and
pharmaceutical industries, as well as clinical and hospital settings.

提出了一个新的框架，将 CNN 和 FL 与 IoBNT 的能力相结合，以提高生物技术中数字孪生的可靠性和安全性。