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 Random Decrement Technique （RDT）, based on decentralized computing approaches implemented in wireless sensor networks （WSNs）, has shown advantages for modal parameter and data aggregation identification. Howev...The Random Decrement Technique （RDT）, based on decentralized computing approaches implemented in wireless sensor networks （WSNs）, has shown advantages for modal parameter and data aggregation identification. However, previous studies of RDT-based approaches from ambient vibration data are based on the assumption of a broad-band stochastic process input excitation. The process normally is modeled by filtered white or white noise. In addition, the choice of the triggering condition in RDT is closely related to data communication. In this project, research has been conducted to study the nonstationary white noise excitations as the input to verify the random decrement technique. A local extremum triggering condition is chosen and implemented for the purpose of minimum data communication in a RDT-based distributed computing strategy. Numerical simulation results show that the proposed technique is capable of minimizing the amount of data transmitted over the network with accuracy in modal parameters identification.展开更多
Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing demands.As any user or enterprise can...Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing demands.As any user or enterprise can be the cloud provider in the decentralized cloud,the performance assessment of the heterogeneous computing resources is of vital significance.However,with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric,the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized cloud.In this paper,we present a robust performance assessment solution RODE to solve this problem.RODE mainly consists of a performance monitoring mechanism and an assessment of the claimed performance(AoCP)mechanism.The performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud providers.Based on the performance monitoring results,the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as claimed.Via extensive experiments,we show RODE can accurately monitor the performance of cloud providers on the premise of reliability,and incentivize cloud providers to honestly present the performance information and maintain the performance stability.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.
基金National Key Technology R&D Program of China under Grant No.2014BAL05B06Guangdong Science&Technology Program under Grant No.2014A050503016the Shenzhen Science&Technology Program under Grant No.GJHZ20150312114346635
文摘The Random Decrement Technique （RDT）, based on decentralized computing approaches implemented in wireless sensor networks （WSNs）, has shown advantages for modal parameter and data aggregation identification. However, previous studies of RDT-based approaches from ambient vibration data are based on the assumption of a broad-band stochastic process input excitation. The process normally is modeled by filtered white or white noise. In addition, the choice of the triggering condition in RDT is closely related to data communication. In this project, research has been conducted to study the nonstationary white noise excitations as the input to verify the random decrement technique. A local extremum triggering condition is chosen and implemented for the purpose of minimum data communication in a RDT-based distributed computing strategy. Numerical simulation results show that the proposed technique is capable of minimizing the amount of data transmitted over the network with accuracy in modal parameters identification.
基金This work is supported by the National Natural Science Foundation of China under Grant Nos.61832006 and 61872240。
文摘Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing demands.As any user or enterprise can be the cloud provider in the decentralized cloud,the performance assessment of the heterogeneous computing resources is of vital significance.However,with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric,the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized cloud.In this paper,we present a robust performance assessment solution RODE to solve this problem.RODE mainly consists of a performance monitoring mechanism and an assessment of the claimed performance(AoCP)mechanism.The performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud providers.Based on the performance monitoring results,the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as claimed.Via extensive experiments,we show RODE can accurately monitor the performance of cloud providers on the premise of reliability,and incentivize cloud providers to honestly present the performance information and maintain the performance stability.
