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Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks 3

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

引用

3rd IEEE International Mediterranean conference on communications and Networking, Meditcom 2023

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

ISBN: (纸本)9798350333732

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. © 2023 IEEE.

关键词： Radio access networks

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

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

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

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

ISBN: (纸本)9798350386226

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. © 2024 IEEE.

关键词： contrastive Learning

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Using Multi-feature Embedding towards Accurate Knowledge Tracing 34

Using Multi-feature Embedding towards Accurate Knowledge Tra...

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34th International conference on software engineering and Knowledge engineering, SEKE 2022

作者： Yu, Yang Huang, Caidie Chen, Liangyu Chen, Mingsong MoE Engineering Research Center of Software Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)1891706543

Knowledge tracing is a crucial task in intelligent tutoring systems. Aiming at the shortcomings of traditional knowledge tracing technology such as low prediction accuracy, overfitting and low utilization of multi-features, this paper proposes a knowledge tracing model SRGCA-M using multi-feature embedding with stacked residual GRU network. compared with the traditional methods that only use the historical record of answering exercises, our approach utilizes a variety of features in the learning process of students to deep characterize students' learning. We increase the layers number of GRU network to expand the capacity of sequence learning and use residual connections to solve the problems of network degradation and vanishing gradient. We use the auto-encoder to solve the problem that the cross-feature encoding will rapidly increase the dimension of the input data. comprehensive experimental results demonstrate that compared with various advanced techniques, our approach can not only achieve better performance of tracking knowledge changes of students but also fully utilize multi-feature information of students in the learning process. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Students

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Throughput-Efficient communication Device Driver for IoT Gateways

Throughput-Efficient Communication Device Driver for IoT Gat...

引用

2022 IEEE International conference on Systems, Man, and Cybernetics, SMC 2022

作者： Niu, Yannian Zhu, Minghua East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

As bridges for data exchange between end devices and cloud servers, gateways play an important role in IoT and cloud computing fields. However, with the dramatic increase of data generated by the end devices in recent years, the limited data forwarding capability of the gateway has become the bottleneck of the performance of the IoT cloud-based architectures. To efficiently improve the data throughput of links between end devices and cloud servers, this paper proposes a user-space driver for gateways that places the packet-forwarding behavior in the user space of the gateway OS and achieves high throughput by bypassing the kernel and simplifying the packet delivery path. In addition, to obtain more significant data throughput in small packet-intensive scenarios, we integrate packet assembling methods for the driver, which assembles received small packets into a large one before forwarding it. Our evaluation shows that the user-space driver has tens of times throughput compared to the kernel driver. Furthermore, our driver cooperated with the packet assembling method shows a significant advantage compared to the latest related work in small packet-intensive scenarios. © 2022 IEEE.

关键词： Cloud computing

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Winograd-CNN Accelerator compatible with Sparse and Non-sparse Models 23

Winograd-CNN Accelerator Compatible with Sparse and Non-spar...

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23rd IEEE International conference on High Performance computing and communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Huazhen Chen, Weiting Wang, Jiangtao East China Normal University Hangzhou Hikvision Digital Technology Company Ltd Hikvision Research Institute Moe Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University Moe Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9781665494571

The inference speed of convolutional neural networks is a key factor for their application in the industry. The two research directions of non-sparse model acceleration and sparse model acceleration are now studied independently. In this paper, we propose a hybrid granular pruning method and a fixed-length binary pruning index scheme, based on which a compatible acceleration architecture is further designed. The hybrid granular pruning method is realized by executing fine-grained pruning after coarse-grained pruning. The binary pruning index scheme ensures a simple fixed-length binary mask to index valid weight positions no matter the model is pruned or not, thus makes it possible for an accelerator compatible with both non-sparse models without pruning and sparse models with hybrid granular pruning. An acceleration architecture is further designed, using a new data layout, a buffer-pipeline mechanism for the data buffering, and converting the Winograd algorithm into matrix operations with parallelism carried out from three aspects. The experimental results suggest that, our proposed acceleration solution shows not only better acceleration performance but also excellent compatibility, and it can be directly applied for the acceleration of models at different pruning levels without recompilation. © 2021 IEEE.

关键词： Industries Smart cities Layout Optimization methods Parallel processing Data models Sparse matrices

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Improvement of knowledge tracing by integrating exercise features and relationships of concepts 3

Improvement of knowledge tracing by integrating exercise fea...

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2023 3rd International conference on Artificial Intelligence and Industrial technology applications, AIITA 2023

作者： Chen, Lige Liu, Xianzhong School of Software Engineering East China Normal University Shanghai China Software/Hardware Co-design Engineering Research Center MoE Shanghai China

Knowledge Tracing (KT) is a challenging task in personalized learning where the objective is to track the progression of students' understanding of the concepts over time based on their learning history. Typically, there are many more exercises than knowledge points. Tracing the state of the concepts seems a reasonable approach but ignores the additional information contained in the exercise itself. Moreover, there are numerous relationships between knowledge points, and the modification of one point's state can affect related points. In this paper, we introduce a method that splits exercises into constituent knowledge points, and incorporates the exercise features to improve the differential representation of the exercises containing the same concepts. We also describe two ways of propagating knowledge point states: one-way and two-way, which update the state of the current knowledge point and its related knowledge points. Our model is tested on two real datasets, and the experimental results show that our model outperforms the existing methods. © Published under licence by IOP Publishing Ltd.

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Joint computing Offloading and Resource Allocation in MEC-Enabled IoT: A Diffusion-Based Reinforcement Learning Approach

Joint Computing Offloading and Resource Allocation in MEC-En...

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IEEE International conference on Systems, Man and Cybernetics

作者： Huimin Cao Bo Xiao MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The integration of the Internet of Things (IoT) with mobile edge computing (MEC) has come out to be a promising solution to address the requirements of high computing capabilities and low latency services, enabling user equipments(UE) to migrate the computation of tasks onto edge servers. This paper focuses on optimizing the performance of MEC-enabled IoT system by formulating a joint computing offloading and resource allocation problem. The objective is to minimize the total delay of the system consisting of multiple servers and multiple users. The denoising network of a diffusion model with capabilities of generation can be trained to obtain optimal solution given the changed environment conditions. Therefore, we propose the diffusion-based deep deterministic policy gradient (DiffDDPG) algorithm which utilizes a diffusion model as the policy to learn optimal decisions jointly. Simulation results exhibits the superior performance of the DiffDDPG algorithm.

关键词： Heuristic algorithms Simulation Training data Reinforcement learning Diffusion models Delays Resource management Servers Internet of Things Optimization

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CA-Res2UNet++: a Deep Residual UNet Based Method for Brain Tumor Segmentation in Multimodal MRI 2

CA-Res2UNet++: a Deep Residual UNet Based Method for Brain T...

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2nd International conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Hu, Yujiao Chen, Weiting Wu, Wenjing Long, Jianghao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

In recent years, segmentation of the multimodal brain tumor image puts forward high requirements for performance. To meet the accuracy requirements, we propose a multimodal brain tumor image segmentation method based on UNet and deep residual learning, which can make full use of multimodal information in order to obtain details. The model achieves the information extraction and integration of multimodal images at different scales by introducing a multiscale feature extraction module, a coordinate attention module, and a dense atrous spatial pyramid pooling module. The results show that the proposed method can fully exploit the multi-scale context information of multimodal images, alleviate the problem of blurred edges in segmentation regions, and achieve good performance on popular evaluation metrics. © 2023 SPIE.

关键词： Magnetic resonance imaging

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Multi-scale Transformation Based Image Representation and Analysis of Amplitude-integrated EEG 2

Multi-scale Transformation Based Image Representation and An...

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2nd International conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Wu, Wenjing Chen, Weiting Hu, Yujiao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

Amplitude-integrated electroencephalography (aEEG) is widely adopted for recognizing neonatal neurological disorders in clinics. Previous work has mainly analyzed aEEGs from a time series perspective, while clinicians are more concerned about images. This paper studies the efficacy of image representations in aEEG interpretation. To this end, we employ Amplitude-frequency contour map (AFCM) to express information on local amplitude distributions and overall temporal trend. During its generation, Multi-scale Box-cox transformation is introduced to flexibly control the degree of amplitude compression and capture details in different amplitude regions. Moreover, one- and two-dimensional features are extracted and combined to enhance model predictions. The experimental results show the potential of image representation for aEEG recognition. This method can provide new ideas to analyze time series signals like aEEGs from the perspective of combining time series and image. © 2023 SPIE.

关键词： Electroencephalography

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Satellite Federated Fine-Tuning for Foundation Models: Architecture design and System Optimization

Satellite Federated Fine-Tuning for Foundation Models: Archi...

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2024 IEEE Global communications conference, GLOBEcoM 2024

作者： Zhu, Yan Yang, Peng Zhu, Jingyang Wen, Dingzhu Wang, Ting Zhou, Yong Shi, Yuanming Jiang, Chunxiao East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350351255

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite. © 2024 IEEE.

关键词： Satellite ground stations

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