Learning the optimal partition for collaborative DNN training with privacy requirements
With the growth of intelligent Internet of Things (IoT) applications and services, deep neural
network (DNN) has become the core method to power and enable increased functionality in …
network (DNN) has become the core method to power and enable increased functionality in …
RL-PDNN: Reinforcement learning for privacy-aware distributed neural networks in IoT systems
Due to their high computational and memory demand, deep learning applications are mainly
restricted to high-performance units, eg, cloud and edge servers. Particularly, in Internet of …
restricted to high-performance units, eg, cloud and edge servers. Particularly, in Internet of …
Privacy-aware edge computing based on adaptive DNN partitioning
Recent years have witnessed deep neural networks (DNNs) become the de facto tool in
many applications such as image classification and speech recognition. But significant …
many applications such as image classification and speech recognition. But significant …
Efficient privacy-preserving federated learning for resource-constrained edge devices
A large volume of data is generated by ubiquitous Internet-of-Things (IoT) devices and
utilized to train machine learning models by IoT manufacturers to provide users with better …
utilized to train machine learning models by IoT manufacturers to provide users with better …
Toward collaborative inferencing of deep neural networks on Internet-of-Things devices
Recent advancements in deep neural networks (DNNs) have enabled us to solve
traditionally challenging problems. To deploy a service based on DNNs, since DNNs are …
traditionally challenging problems. To deploy a service based on DNNs, since DNNs are …
Approximate to be great: Communication efficient and privacy-preserving large-scale distributed deep learning in Internet of Things
The increasing Internet-of-Things (IoT) devices have produced large volumes of data. A
deep learning technique is widely used to analyze the potential value of these data due to its …
deep learning technique is widely used to analyze the potential value of these data due to its …
RL-DistPrivacy: Privacy-aware distributed deep inference for low latency IoT systems
Although Deep Neural Networks (DNN) have become the backbone technology of several
ubiquitous applications, their deployment in resource-constrained machines, eg, Internet of …
ubiquitous applications, their deployment in resource-constrained machines, eg, Internet of …
Game theory based privacy preserving approach for collaborative deep learning in iot
The exponential growth of Internet of Things (IoT) has become a transcending force in
creating innovative smart devices and connected domains including smart homes …
creating innovative smart devices and connected domains including smart homes …
PADL: Privacy-aware and asynchronous deep learning for IoT applications
As a promising data-driven technology, deep learning has been widely employed in a
variety of Internet-of-Things (IoT) applications. Examples include automated navigation …
variety of Internet-of-Things (IoT) applications. Examples include automated navigation …
Privacy-preserving computation offloading for parallel deep neural networks training
Deep neural networks (DNNs) have brought significant performance improvements to
various real-life applications. However, a DNN training task commonly requires intensive …
various real-life applications. However, a DNN training task commonly requires intensive …