Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-4-22 10:20'''
|time='''2022-4-29 10:20'''
|addr=4th Research Building A527-B
|addr=4th Research Building A527-B
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract = Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients’ knowledge occurs in the gradient space. For example, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients. To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models. The training on each client aims to minimize the classification error on the local data while keeping the resulting local prototypes sufficiently close to the corresponding global ones. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives. In experiments, we propose a benchmark setting tailored for heterogeneous FL, with FedProto outperforming several recent FL approaches on multiple datasets. Code is available at https://github.com/yuetan031/fedproto.
|abstract = This paper presents EMU, a framework that enables the emulation, snipping, and multiplexing of LoRa chirps on commercial IoT devices equipped with low-power sub-GHz transceivers, including those supporting LoRa itself. Chirp snipping consists in artificially removing a sequence of chips and in putting the radio in low-power mode, which allows to reduce energy consumption while still communicating reliably. Chirp multiplexing exploits the gaps introduced by chirp snipping to transmit portions of another chirp on a separate channel, which allows to concurrently transmit two LoRa packets and to increase the throughput. We build EMU as a modular framework and implement support for off-the-shelf LoRa and non-LoRa transceivers. We then evaluate its performance by comparing the reliability, efficiency, and receiver sensitivity achieved by EMU with that of traditional LoRa for different physical layer settings. We finally showcase EMU’s ability to send packets over two channels simultaneously, thereby improving the uplink throughput of LoRaWAN, and demonstrate that even non-LoRa transceivers employing EMU can communicate to a LoRaWAN gateway, enabling new use cases and expanding the applicability of LoRa technology.
|confname= AAAI 2022
|confname= IPSN 2022
|link=https://www.aaai.org/AAAI22Papers/AAAI-6846.YueT.pdf
|link=http://www.carloalbertoboano.com/documents/yang22emu.pdf
|title= FedProto: Federated Prototype Learning across Heterogeneous Clients
|title= EMU: Increasing the Performance and Applicability of LoRa through Chirp Emulation, Snipping, and Multiplexing
|speaker=Jianqi
|speaker=Wenliang
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract = The Edge-based Multi-agent visual SLAM plays a key role in emerging mobile applications such as search-and-rescue, inventory automation, and drone grouping. This algorithm relies on a central node to maintain the global map and schedule agents to execute their individual tasks. However, as the number of agents continues growing, the operational overhead of the visual SLAM system such as data redundancy, bandwidth consumption, and localization errors also scale, which challenges the system scalability. In this paper, we present the design and implementation of SwarmMap, a framework design that scales up collaborative visual SLAM service in edge offloading settings. At the core of SwarmMap are three simple yet effective system modules — a change log-based server-client synchronization mechanism, a priority-aware task scheduler, and a lean representation of the global map that work hand-in-hand to address the data explosion caused by the growing number of agents. We make SwarmMap compatible with the robotic operating system (ROS) and open-source it. Existing visual SLAM applications could incorporate SwarmMap to enhance their performance and capacity in multi-agent scenarios. Comprehensive evaluations and a three-month case study at one of the world's largest oil fields demonstrate that SwarmMap can serve 2× more agents (>20 agents) than the state of the arts with the same resource overhead, meanwhile maintaining an average trajectory error of 38cm, outperforming existing works by > 55%.
|abstract = Containers, originally designed for cloud environments, are increasingly popular for provisioning workers outside the cloud, for example in mobile and edge computing. These settings, however, bring new challenges: high latency links, limited bandwidth, and resource-constrained workers. The result is longer provisioning times when deploying new workers or updating existing ones, much of it due to network traffic. Our analysis shows that current piecemeal approaches to reducing provisioning time are not always sufficient, and can even make things worse as round-trip times grow. Rather, we find that the very same layer-based structure that makes containers easy to develop and use also makes it more difficult to optimize deployment. Addressing this issue thus requires rethinking the container deployment pipeline as a whole. Based on our findings, we present Starlight: an accelerator for container provisioning. Starlight decouples provisioning from development by redesigning the container deployment protocol, filesystem, and image storage format. Our evaluation using 21 popular containers shows that, on average, Starlight deploys and starts containers 3.0x faster than the current state-of-the-art implementation while incurring no runtime overhead and little (5%) storage overhead. Finally, it is backwards compatible with existing workers and uses standard container registries.
|confname= NSDI 2022
|confname= NSDI 2022
|link=https://www.usenix.org/system/files/nsdi22-paper-xu_jingao.pdf
|link=https://www.usenix.org/system/files/nsdi22-paper-chen_jun_lin.pdf
|title=SwarmMap: Scaling Up Real-time Collaborative Visual SLAM at the Edge
|title=Starlight: Fast Container Provisioning on the Edge and over the WAN
|speaker=Jianfei
|speaker=Jiangshu
}}
}}


Revision as of 15:28, 27 April 2022

Time: 2022-4-29 10:20
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [IPSN 2022] EMU: Increasing the Performance and Applicability of LoRa through Chirp Emulation, Snipping, and Multiplexing, Wenliang
    Abstract: This paper presents EMU, a framework that enables the emulation, snipping, and multiplexing of LoRa chirps on commercial IoT devices equipped with low-power sub-GHz transceivers, including those supporting LoRa itself. Chirp snipping consists in artificially removing a sequence of chips and in putting the radio in low-power mode, which allows to reduce energy consumption while still communicating reliably. Chirp multiplexing exploits the gaps introduced by chirp snipping to transmit portions of another chirp on a separate channel, which allows to concurrently transmit two LoRa packets and to increase the throughput. We build EMU as a modular framework and implement support for off-the-shelf LoRa and non-LoRa transceivers. We then evaluate its performance by comparing the reliability, efficiency, and receiver sensitivity achieved by EMU with that of traditional LoRa for different physical layer settings. We finally showcase EMU’s ability to send packets over two channels simultaneously, thereby improving the uplink throughput of LoRaWAN, and demonstrate that even non-LoRa transceivers employing EMU can communicate to a LoRaWAN gateway, enabling new use cases and expanding the applicability of LoRa technology.
  2. [NSDI 2022] Starlight: Fast Container Provisioning on the Edge and over the WAN, Jiangshu
    Abstract: Containers, originally designed for cloud environments, are increasingly popular for provisioning workers outside the cloud, for example in mobile and edge computing. These settings, however, bring new challenges: high latency links, limited bandwidth, and resource-constrained workers. The result is longer provisioning times when deploying new workers or updating existing ones, much of it due to network traffic. Our analysis shows that current piecemeal approaches to reducing provisioning time are not always sufficient, and can even make things worse as round-trip times grow. Rather, we find that the very same layer-based structure that makes containers easy to develop and use also makes it more difficult to optimize deployment. Addressing this issue thus requires rethinking the container deployment pipeline as a whole. Based on our findings, we present Starlight: an accelerator for container provisioning. Starlight decouples provisioning from development by redesigning the container deployment protocol, filesystem, and image storage format. Our evaluation using 21 popular containers shows that, on average, Starlight deploys and starts containers 3.0x faster than the current state-of-the-art implementation while incurring no runtime overhead and little (5%) storage overhead. Finally, it is backwards compatible with existing workers and uses standard container registries.


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

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