Difference between revisions of "Resource:Seminar"

From MobiNetS
Jump to: navigation, search
 
(95 intermediate revisions by 4 users not shown)
Line 1: Line 1:
{{SemNote
{{SemNote
|time='''Thursday 16:20-18:00'''
|time='''2025-03-28 10:30-12:00'''
|addr=4th Research Building A518
|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=Global-scale IPv6 scan, critical for network measurement and management, is still a mission to be accomplished due to its vast address space. To tackle this challenge, IPv6 scan generally leverages pre-defined seed addresses to guide search directions. Under this general principle, however, the core problem of effectively using the seeds is largely open. In this work, we propose a novel IPv6 active search strategy, namely HMap6, which significantly improves the use of seeds, w.r.t. the marginal benefit, for large-scale active address discovery in various prefixes. Using a heuristic search strategy for efficient seed collection and alias prefix detection under a wide range of BGP prefixes, HMap6 can greatly expand the scan coverage. Real-world experiments over the Internet in billion-scale scans show that HMap6 can discover 29.39M unique /80 prefixes with active addresses, an 11.88% improvement over the state-of-the-art methods. Furthermore, the IPv6 hitlists from HMap6 include all-responsive IPv6 addresses with rich information. This result sharply differs from existing public IPv6 hitlists, which contain non-responsive and filtered addresses, and pushes the IPv6 hitlists from quantity to quality. To encourage and benefit further IPv6 measurement studies, we released our tool along with our IPv6 hitlists and the detected alias prefixes.
|abstract = Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing their private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE does not consider the heterogeneity among different clients, an intrinsic problem in cross-silo FL, often resulting in undermined training efficiency with slow convergence and stragglers. In this work, we propose FedPHE, an efficiently packed homomorphically encrypted FL framework with secure weighted aggregation and client selection to tackle the heterogeneity problem. Specifically, using CKKS with sparsification, FedPHE can achieve efficient encrypted weighted aggregation by accounting for contributions of local updates to the global model. To mitigate the straggler effect, we devise a sketching-based client selection scheme to cherry-pick representative clients with heterogeneous models and computing capabilities. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients’ privacy, achieve a training speedup of 1.85 − 4.44×, cut the communication overhead by 1.24 − 22.62× , and reduce the straggler effect by up to 1.71 − 2.39×.
|confname=INFOCOM '23
|confname =INFOCOM24'
|link=https://ieeexplore.ieee.org/abstract/document/10229089
|link = https://ieeexplore.ieee.org/abstract/document/10621440
|title=Search in the Expanse: Towards Active and Global IPv6 Hitlists
|title= Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning
|speaker=Xinyu
|speaker=Dongting
|date=2023-11-2}}
|date=2025-03-28
{{Latest_seminar
}}{{Latest_seminar
|abstract=LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential for efficiently deploying a LoRa network in orchards, e.g., determining gateway coverage and sensor placement. Although some propagation models have been studied for LoRa networks, they are not suitable for orchard environments, because they do not consider the shadowing effect on wireless propagation caused by the ground and tree canopies. This paper presents FLog, a propagation model for LoRa signals in orchard environments. FLog leverages a unique feature of orchards, i.e., all trees have similar shapes and are planted regularly in space. We develop a 3D model of the orchards. Once we have the location of a sensor and a gateway, we know the mediums that the wireless signal traverse. Based on this knowledge, we generate the First Fresnel Zone (FFZ) between the sender and the receiver. The intrinsic path loss exponents (PLE) of all mediums can be combined into a classic Log-Normal Shadowing model in the FFZ. Extensive experiments in almond orchards show that FLog reduces the link quality estimation error by 42.7% and improves gateway coverage estimation accuracy by 70.3%, compared with a widely-used propagation model.
|abstract = Entanglement routing (ER) in quantum networks must guarantee entanglement fidelity, a property that is crucial for applications such as quantum key distribution, quantum computation, and quantum sensing. Conventional ER approaches assume that network links can only generate entanglements with a fixed fidelity, and then they rely on purification to improve endto-end fidelities. However, recent advances in entanglement generation technologies show that quantum links can be configured by choosing among different fidelity/entanglement-rate combinations (defined in this paper as link configurations), hence enabling a more flexible assignment of quantum-network resources for meeting specific application requirements. To exploit this opportunity, we introduce the problem of link configuration for fidelityconstrained routing and purification (LC-FCRP) in Quantum Networks. We first formulate a simplified FCRP version as a Mixed Integer Linear Programming (MILP) model, where the link fidelity can be adjusted within a finite set. Then, to explore the full space of possible link configurations, we propose a link configuration algorithm based on a novel shortest-pathbased fidelity determination (SPFD) algorithm w/o Bayesian Optimization, which can be applied on top of any existing ER algorithm. Numerical results demonstrate that link configuration improves the acceptance ratio of existing ER algorithms by 87%.
|confname=IPSN '23
|confname =INFOCOM25'
|link=https://dl.acm.org/doi/10.1145/3583120.3586969
|link = https://re.public.polimi.it/bitstream/11311/1281986/1/final_infocom25_link_configuration_for_entanglement_routing.pdf
|title=Link Quality Modeling for LoRa Networks in Orchards
|title= Link Configuration for Fidelity-Constrained Entanglement Routing in Quantum Networks
|speaker=Jiacheng
|speaker=Yaliang
|date=2023-11-02}}
|date=2025-03-27
{{Latest_seminar
}}
|abstract=Quality of Experience (QoE) is one of the most important quality indicators for video streaming applications. But it is still an open question how to assess QoE value objectively and quantitatively over continuous time both for academia and industry. In this paper, we carry out an extensive data study on user behaviors in one of the largest short-video service providers. The measurement data reveals that the user’s exiting behavior in viewing video streams is an appropriate choice as a continuous-time QoE metric. Secondly, we build a quantitative QoE model to objectively assess the quality of video playback by discretizing the playback session into the Markov chain. By collecting 7 billion viewing session logs which cover users from 20 CDN providers and 40 Internet service providers, the proposed state-chain-based model of State-Exiting Ratio (SER) is validated. The experimental results show that the modeling error of SER and session duration are less than 2% and 10s respectively. By using the proposed scheme to optimize adaptive video streaming, the average session duration is improved up to 60% to baseline, and 20% to the existing black-box-like machine learning methods.
 
|confname=INFOCOM '23
|link=https://ieeexplore.ieee.org/document/10228896
|title=Rebuffering but not Suffering: Exploring Continuous-Time Quantitative QoE by User’s Exiting Behaviors
|speaker=Jiajun
|date=2023-11-02}}
{{Latest_seminar
|abstract=The resource efficiency of video analytics workloads is critical for large-scale deployments on edge nodes and cloud clusters. Recent advanced systems have benefited from techniques including video compression, frame filtering, and deep model acceleration. However, based on our year-long experience of operating a real-time video analytics system on more than 1000 cameras, we identified a previously overlooked bottleneck of end- to-end concurrency: video decoding. To support concurrent video inference at scale, in this work, we investigate a new task, named video packet gating, which selectively filters packets before running a decoder. We propose a novel multi-view embedding approach for video packets and present PacketGame that has both theoretical performance guarantee and practical system designs. Experiments on both public datasets and a real system show PacketGame saves 52.0-79.3% decoding costs and achieves 2.1-4.8× concurrency compared to original workloads. Comparisons with four state-of-the-art complementary methods show the superiority of PacketGame in end-to-end concurrency.
|confname=SIGCOMM '23
|link=https://yuanmu97.github.io/preprint/packetgame_sigcomm23.pdf
|title=PacketGame: Multi-Stream Packet Gating for Concurrent Video Inference at Scale
|speaker=Shuhong
|date=2023-11-02}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 23:10, 27 March 2025

Time: 2025-03-28 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [INFOCOM24'] Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning, Dongting
    Abstract: Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing their private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE does not consider the heterogeneity among different clients, an intrinsic problem in cross-silo FL, often resulting in undermined training efficiency with slow convergence and stragglers. In this work, we propose FedPHE, an efficiently packed homomorphically encrypted FL framework with secure weighted aggregation and client selection to tackle the heterogeneity problem. Specifically, using CKKS with sparsification, FedPHE can achieve efficient encrypted weighted aggregation by accounting for contributions of local updates to the global model. To mitigate the straggler effect, we devise a sketching-based client selection scheme to cherry-pick representative clients with heterogeneous models and computing capabilities. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients’ privacy, achieve a training speedup of 1.85 − 4.44×, cut the communication overhead by 1.24 − 22.62× , and reduce the straggler effect by up to 1.71 − 2.39×.
  2. [INFOCOM25'] Link Configuration for Fidelity-Constrained Entanglement Routing in Quantum Networks, Yaliang
    Abstract: Entanglement routing (ER) in quantum networks must guarantee entanglement fidelity, a property that is crucial for applications such as quantum key distribution, quantum computation, and quantum sensing. Conventional ER approaches assume that network links can only generate entanglements with a fixed fidelity, and then they rely on purification to improve endto-end fidelities. However, recent advances in entanglement generation technologies show that quantum links can be configured by choosing among different fidelity/entanglement-rate combinations (defined in this paper as link configurations), hence enabling a more flexible assignment of quantum-network resources for meeting specific application requirements. To exploit this opportunity, we introduce the problem of link configuration for fidelityconstrained routing and purification (LC-FCRP) in Quantum Networks. We first formulate a simplified FCRP version as a Mixed Integer Linear Programming (MILP) model, where the link fidelity can be adjusted within a finite set. Then, to explore the full space of possible link configurations, we propose a link configuration algorithm based on a novel shortest-pathbased fidelity determination (SPFD) algorithm w/o Bayesian Optimization, which can be applied on top of any existing ER algorithm. Numerical results demonstrate that link configuration improves the acceptance ratio of existing ER algorithms by 87%.

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

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=3337"