Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-4-22 10:20'''
|time='''2024-12-06 10:30-12:00'''
|addr=4th Research Building A527-B
|addr=4th Research Building A518
|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]].
}}
}}


===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 = Packet routing in virtual networks requires virtual-to-physical address translation. The address mappings are updated by a single party, i.e., the network administrator, but they are read by multiple devices across the network when routing tenant packets. Existing approaches face an inherent read-write performance tradeoff: they either store these mappings in dedicated gateways for fast updates at the cost of slower forwarding or replicate them at end-hosts and suffer from slow updates.SwitchV2P aims to escape this tradeoff by leveraging the network switches to transparently cache the address mappings while learning them from the traffic. SwitchV2P brings the mappings closer to the sender, thus reducing the first packet latency and translation overheads, while simultaneously enabling fast mapping updates, all without changing existing routing policies and deployed gateways. The topology-aware data-plane caching protocol allows the switches to transparently adapt to changing network conditions and varying in-switch memory capacity.Our evaluation shows the benefits of in-network address mapping, including an up to 7.8× and 4.3× reduction in FCT and first packet latency respectively, and a substantial reduction in translation gateway load. Additionally, SwitchV2P achieves up to a 1.9× reduction in bandwidth overheads and requires order-of-magnitude fewer gateways for equivalent performance.
|confname= AAAI 2022
|confname =SIGCOMM'24
|link=https://www.aaai.org/AAAI22Papers/AAAI-6846.YueT.pdf
|link = https://dl.acm.org/doi/abs/10.1145/3651890.3672213
|title= FedProto: Federated Prototype Learning across Heterogeneous Clients
|title= In-Network Address Caching for Virtual Networks
|speaker=Jianqi
|speaker=Dongting
}}
|date=2024-12-06
{{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 = Visible light communication (VLC) has become an important complementary means to electromagnetic communications due to its freedom from interference. However, existing Internet-of-Things (IoT) VLC links can reach only <10 meters, which has significantly limited the applications of VLC to the vast and diverse scenarios. In this paper, we propose ChirpVLC, a novel modulation method to prolong VLC distance from ≤10 meters to over 100 meters. The basic idea of ChirpVLC is to trade throughput for prolonged distance by exploiting Chirp Spread Spectrum (CSS) modulation. Specifically, 1) we modulate the luminous intensity as a sinusoidal waveform with a linearly varying frequency and design different spreading factors (SF) for different environmental conditions. 2) We design range adaptation scheme for luminance sensing range to help receivers achieve better signal-to-noise ratio (SNR). 3) ChirpVLC supports many-to-one and non-line-of-sight communications, breaking through the limitations of visible light communication. We implement ChirpVLC and conduct extensive real-world experiments. The results show that ChirpVLC can extend the transmission distance of 5W COTS LEDs to over 100 meters, and the distance/energy utility is increased by 532% compared to the existing work.
|confname= NSDI 2022
|confname = IDEA
|link=https://www.usenix.org/system/files/nsdi22-paper-xu_jingao.pdf
|link = https://uestc.feishu.cn/file/Pbq3bWgKJoTQObx79f3cf6gungb
|title=SwarmMap: Scaling Up Real-time Collaborative Visual SLAM at the Edge
|title= ChirpVLC：Extending The Distance of Low-cost Visible Light Communication with CSS Modulation
|speaker=Jianfei
|speaker=Mengyu
|date=2024-12-06
}}
}}


=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 11:28, 6 December 2024

Time: 2024-12-06 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [SIGCOMM'24] In-Network Address Caching for Virtual Networks, Dongting
    Abstract: Packet routing in virtual networks requires virtual-to-physical address translation. The address mappings are updated by a single party, i.e., the network administrator, but they are read by multiple devices across the network when routing tenant packets. Existing approaches face an inherent read-write performance tradeoff: they either store these mappings in dedicated gateways for fast updates at the cost of slower forwarding or replicate them at end-hosts and suffer from slow updates.SwitchV2P aims to escape this tradeoff by leveraging the network switches to transparently cache the address mappings while learning them from the traffic. SwitchV2P brings the mappings closer to the sender, thus reducing the first packet latency and translation overheads, while simultaneously enabling fast mapping updates, all without changing existing routing policies and deployed gateways. The topology-aware data-plane caching protocol allows the switches to transparently adapt to changing network conditions and varying in-switch memory capacity.Our evaluation shows the benefits of in-network address mapping, including an up to 7.8× and 4.3× reduction in FCT and first packet latency respectively, and a substantial reduction in translation gateway load. Additionally, SwitchV2P achieves up to a 1.9× reduction in bandwidth overheads and requires order-of-magnitude fewer gateways for equivalent performance.
  2. [IDEA] ChirpVLC：Extending The Distance of Low-cost Visible Light Communication with CSS Modulation, Mengyu
    Abstract: Visible light communication (VLC) has become an important complementary means to electromagnetic communications due to its freedom from interference. However, existing Internet-of-Things (IoT) VLC links can reach only <10 meters, which has significantly limited the applications of VLC to the vast and diverse scenarios. In this paper, we propose ChirpVLC, a novel modulation method to prolong VLC distance from ≤10 meters to over 100 meters. The basic idea of ChirpVLC is to trade throughput for prolonged distance by exploiting Chirp Spread Spectrum (CSS) modulation. Specifically, 1) we modulate the luminous intensity as a sinusoidal waveform with a linearly varying frequency and design different spreading factors (SF) for different environmental conditions. 2) We design range adaptation scheme for luminance sensing range to help receivers achieve better signal-to-noise ratio (SNR). 3) ChirpVLC supports many-to-one and non-line-of-sight communications, breaking through the limitations of visible light communication. We implement ChirpVLC and conduct extensive real-world experiments. The results show that ChirpVLC can extend the transmission distance of 5W COTS LEDs to over 100 meters, and the distance/energy utility is increased by 532% compared to the existing work.

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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