Joint placement and routing for fixed-partition DNN inference over multi-hop edge networks is addressed with an alternating optimization framework that shows split flexibility matters most in IoT-edge-cloud settings and congestion awareness helps as load increases.
Distributed deep neural networks over the cloud, the edge and end devices
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The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.
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Design Insights into Partition Placement and Routing for DNN Inference in Multi-Hop Edge Networks
Joint placement and routing for fixed-partition DNN inference over multi-hop edge networks is addressed with an alternating optimization framework that shows split flexibility matters most in IoT-edge-cloud settings and congestion awareness helps as load increases.
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Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey
The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.