DeepSwarm: towards swarm deep learning with bi-directional optimization of data acquisition and processing

Liu, Sicong; Guo, Bin; Wang, Ziqi; Wang, Lehao; Zhou, Zimu; Li, Xiaochen; Yu, Zhiwen

doi:10.1007/s11704-024-40465-z

Front. Comput. Sci.

2024

DOI: 10.1007/s11704-024-40465-z

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DeepSwarm: towards swarm deep learning with bi-directional optimization of data acquisition and processing

Sicong Liu,

Bin Guo,

Ziqi Wang

et al.

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AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control

Wu,

Liu,

Zhu

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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The rise of mobile devices equipped with numerous sensors, such as LiDAR and cameras, has spurred the adoption of multi-modal deep intelligence for distributed sensing tasks, such as smart cabins and driving assistance. However, the arrival times of mobile sensory data vary due to modality size and network dynamics, which can lead to delays (if waiting for slower data) or accuracy decline (if inference proceeds without waiting). Moreover, the diversity and dynamic nature of mobile systems exacerbate this challenge. In response, we present a shift to opportunistic inference for asynchronous distributed multi-modal data, enabling inference as soon as partial data arrives. While existing methods focus on optimizing modality consistency and complementarity, known as modal affinity, they lack a computational approach to control this affinity in open-world mobile environments. AdaFlow pioneers the formulation of structured cross-modality affinity in mobile contexts using a hierarchical analysis-based normalized matrix. This approach accommodates the diversity and dynamics of modalities, generalizing across different types and numbers of inputs. Employing an affinity attention-based conditional GAN (ACGAN), AdaFlow

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AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control

Wu,

Liu,

Zhu

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

View full text Add to dashboard Cite

show abstract

Non-blocking Inference on Asynchronous Mobile Sensor Data with Affinity Control

2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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DeepSwarm: towards swarm deep learning with bi-directional optimization of data acquisition and processing

Cited by 2 publications

References 4 publications

AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control

AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control

Non-blocking Inference on Asynchronous Mobile Sensor Data with Affinity Control

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