With the growing demand for real-time intelligence driven by device-to-device (D2D)
communication, deploying Deep Learning (DL) applications at the network edge becomes …

With significant potential improvement in device-to-device (D2D) communication due to
improved wireless link capacity (eg, 5G and NextG systems), a collaboration of multiple …

With the continuous expansion of neural networks in size and depth and the growing
popularity of machine learning as a service collaborative inference systems present a …

Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep
learning applications without disclosing their raw data to the cloud server, thus protecting …

Collaborative inference has been a promising solution to enable resource-constrained edge
devices to perform inference using state-of-the-art deep neural networks (DNNs). In …

Collaborative inference leverages diverse features provided by different agents (eg,
sensors) for more accurate inference. A common setup is where each agent sends its …

Deep learning models have exhibited remarkable performance across various domains.
Nevertheless, the burgeoning model sizes compel edge devices to offload a significant …

To ensure that accuracy and latency are not compromised while deploying Deep Neural
Networks (DNNs) on edge devices, trained DNN models can be partitioned across many …

Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep
learning applications without disclosing their raw data to the cloud server, thus preserving …

Deep Learning (DL) has been widely adopted in almost all domains, from threat recognition
to medical diagnosis. Albeit its supreme model accuracy, DL imposes a heavy burden on …