Difference between revisions of "Resource:Seminar"

From MobiNetS
Jump to: navigation, search
(wenliang updates seminar)
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=With recent advances, neural networks have become a crucial building block in intelligent IoT systems and sensing applications. However, the excessive computational demand remains a serious impediment to their deployments on low-end IoT devices. With the emergence of edge computing, offloading grows into a promising technique to circumvent end-device limitations. However, transferring data between local and edge devices takes up a large proportion of time in existing offloading frameworks, creating a bottleneck for low-latency intelligent services. In this work, we propose a general framework, called deep compressive offloading. By integrating compressive sensing theory and deep learning, our framework can encode data for offloading into tiny sizes with negligible overhead on local devices and decode the data on the edge server, while offering theoretical guarantees on perfect reconstruction and lossless inference. By trading edge computing resources for data transmission time, our design can significantly reduce offloading latency with almost no accuracy loss. We build a deep compressive offloading system to serve state-of-the-art computer vision and speech recognition services. With comprehensive evaluations, our system can consistently reduce end-to-end latency by 2X to 4X with 1% accuracy loss, compared to state-of-the-art neural network offloading systems. In conditions of limited network bandwidth or intensive background traffic, our system can further speed up the neural network inference by up to 35X 1.
|abstract=In vehicular ad hoc networks (VANETs), quick and reliable multi-hop broadcasting is important for the dissemination of emergency warning messages. By scheduling multiple nodes to transmit messages concurrently and cooperatively, cooperative transmission based broadcast schemes may yield much better broadcast performance than conventional broadcast schemes. However, a cooperative transmission requires multiple relays to achieve strict synchronization on both time and frequency, which may induce high cost for a cooperative transmission process. In this paper, we analyze the cost and benefit of a cooperative transmission for data broadcasting in vehicular networks, and introduce a new metric called the single-hop broadcast efficiency (SBE) to evaluate the overall broadcast performance. We propose an efficient, non-deterministic cooperation mechanism to reduce the cooperation cost. The mechanism maximizes the expected broadcast performance by selecting cooperators with the largest expected SBE value for a lead relay, and initiates cooperative broadcasting process when the expected SBE value is larger than that of a single-relay based broadcasting. Based on the non-deterministic mechanism, we propose an efficient, cooperative transmission based opportunistic broadcast (ECTOB) scheme which further utilizes rebroadcast to improve the reliability of the broadcast scheme. Simulation results show that the proposed scheme outperforms the conventional ones.
|confname=SenSys 2020
|confname=TMC 2023
|link=https://dl.acm.org/doi/pdf/10.1145/3384419.3430898
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9519523
|title=Deep compressive offloading: speeding up neural network inference by trading edge computation for network latency
|title=An Efficient Cooperative Transmission Based Opportunistic Broadcast Scheme in VANETs
|speaker=Crong}}
|speaker=Luwei}}
{{Latest_seminar
{{Latest_seminar
|abstract = We propose and implement Directory-Based Access Control (DBAC), a flexible and systematic access control approach for geographically distributed multi-administration IoT systems. DBAC designs and relies on a particular module, IoT directory, to store device metadata, manage federated identities, and assist with cross-domain authorization. The directory service decouples IoT access into two phases: discover device information from directories and operate devices through discovered interfaces. DBAC extends attribute-based authorization and retrieves diverse attributes of users, devices, and environments from multi-faceted sources via standard methods, while user privacy is protected. To support resource-constrained devices, DBAC assigns a capability token to each authorized user, and devices only validate tokens to process a request.
|abstract = Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
|confname=INFOCOM 2022
|confname=CVPR 2022
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796804
|link=https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.pdf4
|title=DBAC: Directory-Based Access Control for Geographically Distributed IoT Systems
|title=Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
|speaker=Xinyu}}
|speaker=Jiaqi}}
{{Latest_seminar
{{Latest_seminar
|abstract = Edge computing is being widely used for video analytics. To alleviate the inherent tension between accuracy and cost, various video analytics pipelines have been proposed to optimize the usage of GPU on edge nodes. Nonetheless, we find that GPU compute resources provisioned for edge nodes are commonly under-utilized due to video content variations, subsampling and filtering at different places of a video analytics pipeline. As opposed to model and pipeline optimization, in this work, we study the problem of opportunistic data enhancement using the non-deterministic and fragmented idle GPU resources. In specific, we propose a task-specific discrimination and enhancement module, and a model-aware adversarial training mechanism, providing a way to exploit idle resources to identify and transform pipeline-specific, low-quality images in an accurate and efficient manner. A multi-exit enhancement model structure and a resource-aware scheduler is further developed to make online enhancement decisions and fine-grained inference execution under latency and GPU resource constraints. Experiments across multiple video analytics pipelines and datasets reveal that our system boosts DNN object detection accuracy by 7.27 -- 11.34% by judiciously allocating 15.81 -- 37.67% idle resources on frames that tend to yield greater marginal benefits from enhancement.
|abstract = Visible light communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer CoLight aiming to boost the data rate of the VLC system purely built upon COTS devices. CoLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, CoLight interfaces a COTS PD with a smartphone through the audio jack, and it also has an alternative MCU-driven circuit to emulate a future integration into the phone. The evaluations on CoLight are both promising and informative: they demonstrate a throughput up to 80 kbps at a distance of 2 m, while suggesting various potentials to further enhance the performance.judiciously allocating 15.81 -- 37.67% idle resources on frames that tend to yield greater marginal benefits from enhancement.
|confname=SenSys 2022
|confname=TMC 2021
|link=https://dl.acm.org/doi/pdf/10.1145/3560905.3568501
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8978742
|title=Turbo: Opportunistic Enhancement for Edge Video Analytics
|title=Pushing the Data Rate of Practical VLC via Combinatorial Light Emission
|speaker=Jiajun}}
|speaker=Mengyu}}




Revision as of 23:52, 3 May 2023

Time: 2023-04-27 9:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [TMC 2023] An Efficient Cooperative Transmission Based Opportunistic Broadcast Scheme in VANETs, Luwei
    Abstract: In vehicular ad hoc networks (VANETs), quick and reliable multi-hop broadcasting is important for the dissemination of emergency warning messages. By scheduling multiple nodes to transmit messages concurrently and cooperatively, cooperative transmission based broadcast schemes may yield much better broadcast performance than conventional broadcast schemes. However, a cooperative transmission requires multiple relays to achieve strict synchronization on both time and frequency, which may induce high cost for a cooperative transmission process. In this paper, we analyze the cost and benefit of a cooperative transmission for data broadcasting in vehicular networks, and introduce a new metric called the single-hop broadcast efficiency (SBE) to evaluate the overall broadcast performance. We propose an efficient, non-deterministic cooperation mechanism to reduce the cooperation cost. The mechanism maximizes the expected broadcast performance by selecting cooperators with the largest expected SBE value for a lead relay, and initiates cooperative broadcasting process when the expected SBE value is larger than that of a single-relay based broadcasting. Based on the non-deterministic mechanism, we propose an efficient, cooperative transmission based opportunistic broadcast (ECTOB) scheme which further utilizes rebroadcast to improve the reliability of the broadcast scheme. Simulation results show that the proposed scheme outperforms the conventional ones.
  2. [CVPR 2022] Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning, Jiaqi
    Abstract: Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  3. [TMC 2021] Pushing the Data Rate of Practical VLC via Combinatorial Light Emission, Mengyu
    Abstract: Visible light communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer CoLight aiming to boost the data rate of the VLC system purely built upon COTS devices. CoLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, CoLight interfaces a COTS PD with a smartphone through the audio jack, and it also has an alternative MCU-driven circuit to emulate a future integration into the phone. The evaluations on CoLight are both promising and informative: they demonstrate a throughput up to 80 kbps at a distance of 2 m, while suggesting various potentials to further enhance the performance.judiciously allocating 15.81 -- 37.67% idle resources on frames that tend to yield greater marginal benefits from enhancement.


History

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

Template loop detected: Resource:Previous Seminars

Instructions

请使用Latest_seminar和Hist_seminar模板更新本页信息.

    • 修改时间和地点信息
    • 将当前latest seminar部分的code复制到这个页面中
    • 将{{Latest_seminar... 修改为 {{Hist_seminar...，并增加对应的日期信息|date=
    • 填入latest seminar各字段信息
    • link请务必不要留空，如果没有link则填本页地址 https://mobinets.org/index.php?title=Resource:Seminar
  • 格式说明
    • Latest_seminar:

{{Latest_seminar
|confname=
|link=
|title=
|speaker=
}}

    • Hist_seminar

{{Hist_seminar
|confname=
|link=
|title=
|speaker=
|date=
}}

Retrieved from "http://mobinets.cn/site/index.php?title=Resource:Seminar&oldid=2620"