Quantitative Timing Analysis for Cyber-Physical Systems Using Uncertainty-Aware Scenario-Based Specifications

Hu, Ming; Zhang, Min; Wei, Tongquan; Chen, Mingsong

doi:10.1109/tcad.2020.3012843

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…As a well-known distributed machine learning paradigm, Federated Learning (FL) [5] enables various clients to train a global AI model without data sharing collaboratively. Due to the advantage of privacy protection, FL has been widely used in mobile computing and WoT applications, such as real-time systems [6], [7], IoT systems [8], [9], and autonomous driving systems [10]. In each FL round, the cloud server dispatches a global model to multiple clients for local training.…”

Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…As a well-known distribution machine learning paradigm, Federated Learning (FL) (McMahan et al 2017;Li et al 2021c;Hu et al 2023b;Wang et al 2023) has been widely used in various applications, such as Internet of Things (IoT) systems (Zhang et al 2020b;Li et al 2021a;Hu et al 2023a;Jia et al 2023;Cui et al 2021), medical health (Rieke et al 2020), and digital finance (Long et al 2020). Traditional FL methods are based on the Federated Averaging (FedAvg) mechanism, which dispatches a global model to multiple local clients and aggregates their trained local models to update the global model.…”

Section: Introductionmentioning

confidence: 99%

FedMut: Generalized Federated Learning via Stochastic Mutation

Hu,

Cao,

et al. 2024

AAAI

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Although Federated Learning (FL) enables collaborative model training without sharing the raw data of clients, it encounters low-performance problems caused by various heterogeneous scenarios. Due to the limitation of dispatching the same global model to clients for local training, traditional Federated Average (FedAvg)-based FL models face the problem of easily getting stuck into a sharp solution, which results in training a low-performance global model. To address this problem, this paper presents a novel FL approach named FedMut, which mutates the global model according to the gradient change to generate several intermediate models for the next round of training. Each intermediate model will be dispatched to a client for local training. Eventually, the global model converges into a flat area within the range of mutated models and has a well-generalization compared with the global model trained by FedAvg. Experimental results on well-known datasets demonstrate the effectiveness of our FedMut approach in various data heterogeneity scenarios.

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Performance balance oriented product structure optimization involving heterogeneous uncertainties in intelligent manufacturing with an industrial network

Zhang

Feng

et al. 2022

Information Sciences

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Quantitative Timing Analysis for Cyber-Physical Systems Using Uncertainty-Aware Scenario-Based Specifications

Cited by 8 publications

References 33 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

FedMut: Generalized Federated Learning via Stochastic Mutation

Performance balance oriented product structure optimization involving heterogeneous uncertainties in intelligent manufacturing with an industrial network

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