Difference between revisions of "Resource:Seminar"

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{{Latest_seminar
{{Latest_seminar
|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×.
|abstract = 在AI革命汹涌来袭的当下连续创业者如何实现底层认知的进化？AI对技术的影响又如何影响到企业决策？报告人何仲潇系云起老和科技有限公司创始人/CEO，四川浙大校友会理事，浙大企业导师，成都市金熊猫B类人才。让我们跟随云起老和的视角感受AI浪潮中的创业进化历程！
|confname =INFOCOM24'
|confname = 创新创业分享会
|link = https://ieeexplore.ieee.org/abstract/document/10621440
|link = https://mobinets.cn/site/Resource:Seminar
|title= Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning
|title= AI革命浪潮中的进化--连续创业者的底层认知进化与创业选择
|speaker=Dongting
|speaker= 何仲潇
|date=2025-03-28
|date=2025-04-11
}}{{Latest_seminar
|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 =INFOCOM25'
|link = https://re.public.polimi.it/bitstream/11311/1281986/1/final_infocom25_link_configuration_for_entanglement_routing.pdf
|title= Link Configuration for Fidelity-Constrained Entanglement Routing in Quantum Networks
|speaker=Yaliang
|date=2025-03-27
}}
}}


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 09:23, 11 April 2025

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

Latest

  1. [创新创业分享会] AI革命浪潮中的进化--连续创业者的底层认知进化与创业选择, 何仲潇
    Abstract: 在AI革命汹涌来袭的当下连续创业者如何实现底层认知的进化？AI对技术的影响又如何影响到企业决策？报告人何仲潇系云起老和科技有限公司创始人/CEO，四川浙大校友会理事，浙大企业导师，成都市金熊猫B类人才。让我们跟随云起老和的视角感受AI浪潮中的创业进化历程！

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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