As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
The 3rd China International Supply Chain Expo(hereinafter referred to as the CISCE)is approaching,and NEXWISE Intelligence China Limited(hereinaf ter refer red to as NEXWISE Intelligence),a repeat exhibitor at the eve...The 3rd China International Supply Chain Expo(hereinafter referred to as the CISCE)is approaching,and NEXWISE Intelligence China Limited(hereinaf ter refer red to as NEXWISE Intelligence),a repeat exhibitor at the event,is ready for the show.In the E4 Hall’s Digital Technology Exhibition Zone,this AI-focused company will showcase its two flagship products and technology systems:“Zhang An Xing”Smart Security and“KuberAI(Producer Platform)”Computing Power Foundation,demonstrating China’s innovative AIdriven efforts in empowering industrial chain security.展开更多
Given the fast growth of intelligent devices, it is expected that a large number of high-stakes artificial intelligence (AI) applications, e. g., drones, autonomous cars, and tac?tile robots, will be deployed at the e...Given the fast growth of intelligent devices, it is expected that a large number of high-stakes artificial intelligence (AI) applications, e. g., drones, autonomous cars, and tac?tile robots, will be deployed at the edge of wireless networks in the near future. Therefore, the intelligent communication networks will be designed to leverage advanced wireless tech?niques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distribut?ed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model infer?ence. The communication efficiency of edge inference systems is further improved by build?ing up a smart radio propagation environment via intelligent reflecting surface.展开更多
By leveraging the data sample diversity,the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process.However,intermediate classifiers of the earl...By leveraging the data sample diversity,the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process.However,intermediate classifiers of the early exits introduce additional computation overhead,which is unfavorable for resource-constrained edge artificial intelligence(AI).In this paper,we propose an early exit prediction mechanism to reduce the on-device computation overhead in a device-edge co-inference system supported by early-exit networks.Specifically,we design a low-complexity module,namely the exit predictor,to guide some distinctly“hard”samples to bypass the computation of the early exits.Besides,considering the varying communication bandwidth,we extend the early exit prediction mechanism for latency-aware edge inference,which adapts the prediction thresholds of the exit predictor and the confidence thresholds of the early-exit network via a few simple regression models.Extensive experiment results demonstrate the effectiveness of the exit predictor in achieving a better tradeoff between accuracy and on-device computation overhead for early-exit networks.Besides,compared with the baseline methods,the proposed method for latency-aware edge inference attains higher inference accuracy under different bandwidth conditions.展开更多
The integration of Artificial Intelligence(AI)with the Internet of Things(IoT)devices has led to the emergence of Edge AI,a transformative solution that enables data processing directly on the IoT devices or"at t...The integration of Artificial Intelligence(AI)with the Internet of Things(IoT)devices has led to the emergence of Edge AI,a transformative solution that enables data processing directly on the IoT devices or"at the edge"of the network.This paper explores the benefits of Edge AI,emphasizing reduced latency,bandwidth conservation,enhanced privacy,and faster decision-making.Despite its advantages,challenges like resource constraints on IoT devices persist.By examining the practical implications of Edge AI in sectors like healthcare and urban development,this study underscores the paradigm shift towards more efficient,secure,and responsive technological ecosystems.展开更多
WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enh...WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.展开更多
The large-scale model(LSM)can handle large-scale data and complex problems,effectively improving the intelligence level of urban intersections.However,the traffic conditions at intersections are becoming increasingly ...The large-scale model(LSM)can handle large-scale data and complex problems,effectively improving the intelligence level of urban intersections.However,the traffic conditions at intersections are becoming increasingly complex,so the intelligent intersection LSMs(I2LSMs)also need to be continuously learned and updated.The traditional cloud-based training method incurs a significant amount of computational and storage overhead,and there is a risk of data leakage.The combination of edge artificial intelligence and federated learning provides an efficient and highly privacy protected computing mode.Therefore,we propose a hierarchical hybrid distributed training mechanism for I2LSM.Firstly,relying on the intelligent intersection system for cloud-network-terminal integration,we constructed an I2LSM hierarchical hybrid distributed training architecture.Then,we propose a hierarchical hybrid federated learning(H2Fed)algorithm that combines the advantages of centralized federated learning and decentralized federated learning.Further,we propose an adaptive compressed sensing algorithm to reduce the communication overhead.Finally,we analyze the convergence of the H2Fed algorithm.Experimental results show that the H2Fed algorithm reduces the communication overhead by 21.6%while ensuring the accuracy of the model.展开更多
In the context of the progressive development of integrated communication and sensing technology,indoor motion detection relying on Wi-Fi Channel State Information(CSI)has attracted significant attention within the in...In the context of the progressive development of integrated communication and sensing technology,indoor motion detection relying on Wi-Fi Channel State Information(CSI)has attracted significant attention within the industry[1-3].Nevertheless,the home environment presents several challenges that impinge on the reliability of CSI.These challenges include interference[4],intricate spatial arrangements,and the instability of device placement.Moreover,motion sensing based on the CSI paths between terminals and routers may encounter problems such as unpredictable effective ranges[5]and difficulties in penetrating walls.To surmount these obstacles,this paper proposes the following technologies:a)adaptive Signal-to-Noise Ratio(SNR)enhancement;b)adversarial sample training;c)a method for refining spatial granularity;and d)a lightweighting approach for edge-side models.These technologies enable the detection of region-specific human activities through Wi-Fi CSI.The experimental findings demonstrate a reduction in false detection rates and the successful implementation of deep-learning models on compact communication equipment.展开更多
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
文摘The 3rd China International Supply Chain Expo(hereinafter referred to as the CISCE)is approaching,and NEXWISE Intelligence China Limited(hereinaf ter refer red to as NEXWISE Intelligence),a repeat exhibitor at the event,is ready for the show.In the E4 Hall’s Digital Technology Exhibition Zone,this AI-focused company will showcase its two flagship products and technology systems:“Zhang An Xing”Smart Security and“KuberAI(Producer Platform)”Computing Power Foundation,demonstrating China’s innovative AIdriven efforts in empowering industrial chain security.
文摘Given the fast growth of intelligent devices, it is expected that a large number of high-stakes artificial intelligence (AI) applications, e. g., drones, autonomous cars, and tac?tile robots, will be deployed at the edge of wireless networks in the near future. Therefore, the intelligent communication networks will be designed to leverage advanced wireless tech?niques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distribut?ed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model infer?ence. The communication efficiency of edge inference systems is further improved by build?ing up a smart radio propagation environment via intelligent reflecting surface.
基金fund of the Hong Kong Polytechnic University(P0038174)。
文摘By leveraging the data sample diversity,the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process.However,intermediate classifiers of the early exits introduce additional computation overhead,which is unfavorable for resource-constrained edge artificial intelligence(AI).In this paper,we propose an early exit prediction mechanism to reduce the on-device computation overhead in a device-edge co-inference system supported by early-exit networks.Specifically,we design a low-complexity module,namely the exit predictor,to guide some distinctly“hard”samples to bypass the computation of the early exits.Besides,considering the varying communication bandwidth,we extend the early exit prediction mechanism for latency-aware edge inference,which adapts the prediction thresholds of the exit predictor and the confidence thresholds of the early-exit network via a few simple regression models.Extensive experiment results demonstrate the effectiveness of the exit predictor in achieving a better tradeoff between accuracy and on-device computation overhead for early-exit networks.Besides,compared with the baseline methods,the proposed method for latency-aware edge inference attains higher inference accuracy under different bandwidth conditions.
文摘The integration of Artificial Intelligence(AI)with the Internet of Things(IoT)devices has led to the emergence of Edge AI,a transformative solution that enables data processing directly on the IoT devices or"at the edge"of the network.This paper explores the benefits of Edge AI,emphasizing reduced latency,bandwidth conservation,enhanced privacy,and faster decision-making.Despite its advantages,challenges like resource constraints on IoT devices persist.By examining the practical implications of Edge AI in sectors like healthcare and urban development,this study underscores the paradigm shift towards more efficient,secure,and responsive technological ecosystems.
基金supported by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0093/2023/RIA2,0145/2023/RIA3).
文摘WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.
基金supported by the National Natural Science Foundation of China(No.62322103)the BUPT Excellent PhD Students Foundation(No.CX2022218).
文摘The large-scale model(LSM)can handle large-scale data and complex problems,effectively improving the intelligence level of urban intersections.However,the traffic conditions at intersections are becoming increasingly complex,so the intelligent intersection LSMs(I2LSMs)also need to be continuously learned and updated.The traditional cloud-based training method incurs a significant amount of computational and storage overhead,and there is a risk of data leakage.The combination of edge artificial intelligence and federated learning provides an efficient and highly privacy protected computing mode.Therefore,we propose a hierarchical hybrid distributed training mechanism for I2LSM.Firstly,relying on the intelligent intersection system for cloud-network-terminal integration,we constructed an I2LSM hierarchical hybrid distributed training architecture.Then,we propose a hierarchical hybrid federated learning(H2Fed)algorithm that combines the advantages of centralized federated learning and decentralized federated learning.Further,we propose an adaptive compressed sensing algorithm to reduce the communication overhead.Finally,we analyze the convergence of the H2Fed algorithm.Experimental results show that the H2Fed algorithm reduces the communication overhead by 21.6%while ensuring the accuracy of the model.
文摘In the context of the progressive development of integrated communication and sensing technology,indoor motion detection relying on Wi-Fi Channel State Information(CSI)has attracted significant attention within the industry[1-3].Nevertheless,the home environment presents several challenges that impinge on the reliability of CSI.These challenges include interference[4],intricate spatial arrangements,and the instability of device placement.Moreover,motion sensing based on the CSI paths between terminals and routers may encounter problems such as unpredictable effective ranges[5]and difficulties in penetrating walls.To surmount these obstacles,this paper proposes the following technologies:a)adaptive Signal-to-Noise Ratio(SNR)enhancement;b)adversarial sample training;c)a method for refining spatial granularity;and d)a lightweighting approach for edge-side models.These technologies enable the detection of region-specific human activities through Wi-Fi CSI.The experimental findings demonstrate a reduction in false detection rates and the successful implementation of deep-learning models on compact communication equipment.
