Conclusion
Inspired by the collective intelligence observed in natural swarms, where individual proactive actions contribute to superior global performance, we advocate for a shift towards Swarm DL. By harnessing the potential of physically adjacent mobile devices in IoT scenarios, we present DeepSwarm, a closed-loop system framework architecture. DeepSwarm facilitates bidirectional optimization between data acquisition and processing, aiming to push the performance boundaries of on-device DL Specifically, DeepSwarm addresses the requirements of proactive Swarm DL by decomposing them into layers: self-organized swarm data acquisition and self-adaptive, self-evolutionary swarm data processing.
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Acknowledgements
The work was supported by the National Science Fund for Distinguished Young Scholars (62025205), the National Natural Science Foundation of China (Grant Nos. 62032020, 62102317), CityU APRC Grant (9610633).
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Competing interests Bin Guo is an Editorial Board member of the journal and a co-author of this article. To minimize bias, he was excluded from all editorial decision-making related to the acceptance of this article for publication. The remaining authors declare no conflict of interest.
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Liu, S., Guo, B., Wang, Z. et al. DeepSwarm: towards swarm deep learning with bi-directional optimization of data acquisition and processing. Front. Comput. Sci. 19, 193501 (2025). https://doi.org/10.1007/s11704-024-40465-z
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DOI: https://doi.org/10.1007/s11704-024-40465-z