Friday 28 February 2025
The Industrial Internet of Things (IIoT) has revolutionized the way industries operate, enabling real-time monitoring and control of complex systems. However, this increased connectivity also introduces new challenges, such as noise and heterogeneity in sensor data. To address these issues, researchers have developed a novel approach to federated learning, which enables multiple devices to learn from each other without sharing their raw data.
Federated learning is particularly useful for industrial applications where devices may have different capabilities and communication constraints. In these scenarios, traditional centralized approaches can be impractical or even impossible. By leveraging edge computing and deep reinforcement learning, the proposed algorithm, called Denoising and Adaptive Online Vertical Federated Learning (DAO- VFL), enables devices to learn from each other while adapting to changing conditions.
The key innovation of DAO-VFL lies in its ability to reduce noise and heterogeneity in sensor data. This is achieved through a denoising autoencoder, which filters out irrelevant information and enhances the quality of the data. Additionally, the algorithm uses deep reinforcement learning to determine local iteration decisions for each device, ensuring that devices with limited capabilities are not overwhelmed.
The effectiveness of DAO-VFL was demonstrated through simulations and experiments on real-world industrial datasets. The results showed significant improvements in learning performance, total latency, and reward compared to traditional federated learning approaches. Furthermore, the algorithm’s ability to adapt to changing conditions enabled it to outperform other methods in scenarios where sensor data quality varied over time.
The implications of DAO-VFL are far-reaching, enabling industries such as manufacturing, logistics, and energy to operate more efficiently and effectively. By leveraging edge computing and deep reinforcement learning, devices can learn from each other without sharing their raw data, reducing the risk of data breaches and improving overall system reliability.
In this approach, multiple devices can collaborate to achieve a common goal while maintaining control over their own data. This is particularly important in industries where data security and privacy are critical concerns. Furthermore, DAO-VFL’s ability to adapt to changing conditions enables it to be applied to a wide range of scenarios, from real-time monitoring and control to predictive maintenance and quality control.
The development of DAO-VFL has significant potential for industrial applications, enabling devices to learn from each other without sharing their raw data. By leveraging edge computing and deep reinforcement learning, industries can improve efficiency, reliability, and security while maintaining control over their own data.
Cite this article: “Federated Learning in Industrial IoT: Enabling Efficient and Secure Collaboration”, The Science Archive, 2025.
Industrial Internet Of Things, Federated Learning, Edge Computing, Deep Reinforcement Learning, Denoising Autoencoder, Sensor Data, Noise Reduction, Heterogeneity, Real-Time Monitoring, Predictive Maintenance
