‘DeepSwarm’ leading a new era of Swarm Deep Learning
image: The system overview of DeepSwarm
Credit: Sicong LIU, Bin GUO, Ziqi WANG, Lehao WANG, Zimu ZHOU, Xiaochen LI, Zhiwen YU
The rise of on-device deep learning (DL) in resource-limited mobile and embedded devices has stimulated various applications. However, existing on-device DL mostly relies on predefined processing patterns for reacting to given input data, resulting in accuracy and resource efficiency bottlenecks. To solve the problems, a research team led by Sicong Liu published their new research on 15 Mar 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposed a collective deep learning framework called 'DeepSwarm'. This discovery aims to break through the performance bottleneck of deep learning on existing devices by integrating swarm intelligence to achieve bidirectional optimization of data acquisition and processing, providing a new solution for deep learning tasks in the Internet of Things (IoT) scenario.
DOI: 10.1007/s11704-024-40465-z
The DeepSwarm framework innovatively integrates swarm intelligence and deep learning, with its core being the bidirectional optimization of data acquisition and processing. On the one hand, through proactive data collection strategies, DeepSwarm can adjust sensor parameters in real-time, optimize data collection, and reduce redundant data; On the other hand, in the data processing stage, DeepSwarm supports dynamic adjustment and adaptive optimization of deep learning models to cope with complex and changing task requirements. The framework also introduces a closed-loop feedback mechanism, which continuously optimizes the data collection strategy through real-time feedback of data processing, forming a virtuous cycle to maximize system performance and minimize resource consumption.
The research team presented preliminary examples of DeepSwarm in two application scenarios: video analysis and federated learning. The results showed significant improvements in accuracy and resource efficiency. In the future, with the further development of IoT technology, the DeepSwarm framework is expected to play an important role in more fields, such as intelligent healthcare, autonomous driving, and industrial automation. Through continuous optimization and improvement, DeepSwarm is expected to solve the challenges brought by dynamic data and resource changes, and promote the widespread deployment and in-depth development of group deep learning in practical applications.
The proposal of the DeepSwarm framework marks an important milestone in the field of swarm deep learning. It not only provides new ideas for solving existing performance bottlenecks in deep learning, but also lays a solid foundation for the efficient application of future intelligent devices in IoT scenarios. With the deepening of research and the maturity of technology, we look forward to DeepSwarm demonstrating its strong potential and value in more fields.