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Theoretical Aspects of software engineering (TASE)

作者： Jia He Min Zhang Kangli He Yannan Guo Yusi Lei Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781509017652

Probabilistic Model-Checking is a standard approach for automatically verifying stochastic systems. However, it becomes expensive or even intractable for classic approaches to verify complex systems. Statistical model-checking was proposed to overcome this limitation. In this paper, we propose a novel statistical model-checking approach which is based on Bayesian point estimation. Together with the Bayesian point estimation and a given conjugate prior distribution, we are able to predict the upper bound of sample size before sampling. We implement our techniques in a tool. Experiential results show that our approach is competitive, even better than other standard approaches in several cases.

关键词： Upper bound Bayes methods Estimation computational modeling Probabilistic logic Standards

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Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

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IEEE conference on Global communications (GLOBEcoM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

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Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks

arXiv

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arXiv 2023年

作者： Wang, Jiali Mao, Yijie Wang, Ting Shi, Yuanming MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China

In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device adopts quantized neural networks (QNNs) to train a local FL model and transmits the quantized model parameter to the remote radio heads (RRHs). Each RRH receives the signals from devices over the wireless link and forwards the signals to the CS via the fronthaul link. We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN. Based on the proposed energy consumption model, we formulate an energy minimization problem that optimizes the fronthaul rate allocation, user transmit power allocation, and QNN precision levels while satisfying the limited fronthaul capacity constraint and ensuring the convergence of the proposed FL model to a target accuracy. To solve this problem, we analyze the convergence rate and propose efficient algorithms based on the alternative optimization technique. Simulation results show that the proposed FL framework can significantly reduce energy consumption compared to other conventional approaches. We draw the conclusion that the proposed framework holds great potential for achieving a sustainable and environmentally-friendly FL in Cloud-RAN. copyright © 2023, The Authors. All rights reserved.

关键词： Radio access networks

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Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks

Green Federated Learning over Cloud-RAN with Limited Frontha...

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communications and Networking (Meditcom), IEEE International Mediterranean conference on

作者： Jiali Wang Yijie Mao Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN.

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Buffer with Feature Tokens: combating confusion in Medical Image continual Learning

Buffer with Feature Tokens: Combating Confusion in Medical I...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Zheming Fan Weiting Chen Jiahao Fan Ruizhi Yu Shanghai Key Laboratory of Trustworthy Computing MOE Research Center of Software/Hardware Co-Design Engineering East China Normal University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Due to its complexity and privacy concerns, medical data is often difficult to collect fully at once. New data emerges with the discovery of new diseases and advances in medical technology, but privacy concerns limit its storage. When deep learning models require training on datasets of new diseases, they often suffer from catastrophic forgetting, where the models’ performance on previously learned data degrades significantly. Recently, pre-trained continual learning methods have been proposed. Unlike earlier continual learning methods, these new approaches do not require learning excessive amounts of new knowledge, furthermore, they can still achieve good performance even when the pre-trained feature extractor is completely frozen. For these approaches, the performance degradation is not primarily due to catastrophic forgetting, but rather confusion. To combat confusion, we propose a method using a buffer with feature tokens (BWFT) which is cheaper and safer than directly storing the original data. Additionally, we propose a feature merge method to further reduce storage and computation costs. In the class-incremental scenario, our method outperforms state-of-the-art methods on multiple medical datasets as well as natural image datasets. code is available at https://***/1345475677/BWFT.

关键词： continuing education Degradation Training Data privacy Privacy costs Feature extraction Data models Biomedical imaging Diseases

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Hyperbolic Graph Diffusion Model

arXiv

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arXiv 2023年

作者： Wen, Lingfeng Tang, Xuan Ouyang, Mingjie Shen, Xiangxiang Yang, Jian Zhu, Daxin Chen, Mingsong Wei, Xian MoE Engineering Research Center of Hardware/Software Co-design Technology and Application East China Normal University China School of Communication and Electronic Engineering East China Normal University China Information Engineering University Quanzhou Normal University China

Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structures due to its exponential growth property. In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. HGDM captures the crucial graph structure distributions by constructing a hyperbolic potential node space that incorporates edge information. Extensive experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a 48% improvement in the quality of graph generation with highly hierarchical structures. copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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A survey of software trustworthiness measurements

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International Journal of Performability engineering 2019年第9期15卷 2364-2372页

作者： Tao, Hongwei Chen, Yixiang Wu, Hengyang Deng, Rumei School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450002 China MoE Engineering Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai200062 China

As people depend more and more on software, there exists an increasing demand for software trustworthiness. The quantification of software trustworthiness can not only help users choose the right software, but also provide evidence for increasing the trustworthiness of the design and implementation of software. It has important practical significance to software development and application. In this paper, software trustworthiness measurements are surveyed from two aspects of product and process. The advances in the product-oriented software trustworthiness measurements are summarized in the view of attributes and behaviors, and the research progress of the process-based software trustworthiness measurements is studied from the perspective of expert experience and process data. Lastly, the challenges of software trustworthiness measurements are analyzed．. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： software design

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Federated Linear Bandit Learning via Over-the-air computation

arXiv

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arXiv 2023年

作者： Wang, Jiali Jiang, Yuning Liu, Xin Wang, Ting Shi, Yuanming MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Automatic Control Laboratory EPFL Switzerland ShanghaiTech University China

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (Aircomp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings. © 2023, CC BY-NC-SA.

关键词： Fading channels

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FedPerturb: covert Poisoning Attack on Federated Learning via Partial Perturbation 26

FedPerturb: Covert Poisoning Attack on Federated Learning vi...

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26th European conference on Artificial Intelligence, ECAI 2023

作者： Jin, Tongsai Fu, Zhihui Meng, Dan Wang, Jun Qi, Yue Cao, Guitao MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Oppo Research Institute China

ISBN: (纸本)9781643684369

Federated learning breaks through the barrier of data owners by allowing them to collaboratively train a federated machine learning model without compromising the privacy of their own data. However, Federation Learning also faces the threat of poisoning attacks, especially from the client model updates, which may impair the accuracy of the global model. To defend against the poisoning attacks, previous work aims to identify the malicious updates in high dimensional spaces. However, we find that the distances in high dimensional spaces cannot identify the changes in a small subset of dimensions, and the small changes may affect the global models severely. Based on this finding, we propose an untargeted poisoning attack under the federated learning setting via the partial perturbations on a small subset of the carefully selected model parameters, and present two attack object selection strategies. We experimentally demonstrate that the proposed attack scheme achieves high attack success rate on five state-of-the-art defense schemes. Furthermore, the proposed attack scheme remains effective at low malicious client ratios and still circumvents three defense schemes with a malicious client ratio as low as 2%. © 2023 The Authors.

关键词： Learning systems

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A logical approach for the schedulability analysis of CCSL 13

A logical approach for the schedulability analysis of CCSL

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13th International Symposium on Theoretical Aspects of software engineering, TASE 2019

作者： Zhang, Yuanrui Mallet, Frederic Zhu, Huibiao Chen, Yixiang MoE Engineering Research Center for Software/Hardware Co-design East China Normal University China I3S Laboratory UMR 7271 CNRS INRIA Université Nice Sophia Antipolis France Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781728133423

The Clock constraint Specification Language (CCSL) is a clock-based formalism for formal specification and analysis of real-time embedded systems. Previous approaches for the schedulability analysis of CCSL specifications are mainly based on model checking or SMT-checking. In this paper we propose a logical approach mainly based on theorem proving. We build a dynamic logic called 'clock-based dynamic logic' (cDL) to capture the CCSL specifications and build a proof calculus to analyze the schedule problem of the specifications. comparing with previous approaches, our method benefits from the dynamic logic that provides a natural way of capturing the dynamic behaviour of CCSL and a divide-and-conquer way for 'decomposing' a complex formula into simple ones for an SMT-checking procedure. Based on cDL, we outline a method for the schedulability analysis of CCSL. We illustrate our theory through one example. © 2019 IEEE.

关键词： Clocks

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