Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-11-05 8:40
|time='''Friday 10:30-12:00'''
|addr=Main Building B1-612
|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=Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving privacy and eliminating the need to store data globally. While there are promising results under the assumption of independent and identically distributed (iid) local data, current state-of-the-art algorithms suffer a performance degradation as the heterogeneity of local data across clients increases. To resolve this issue, we propose a simple framework, \emph{Mean Augmented Federated Learning (MAFL)}, where clients send and receive \emph{averaged} local data, subject to the privacy requirements of target applications. Under our framework, we propose a new augmentation algorithm, named \emph{FedMix}, which is inspired by a phenomenal yet simple data augmentation method, Mixup, but does not require local raw data to be directly shared among devices. Our method shows greatly improved performance in the standard benchmark datasets of FL, under highly non-iid federated settings, compared to conventional algorithms.
|abstract=Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.
|confname=ICLR 2021
|confname=SIGCOMM 2020
|link=https://openreview.net/pdf?id=Ogga20D2HO-
|link=https://dl.acm.org/doi/10.1145/3387514.3405853
|title=FedMix: Approximation of Mixup under Mean Augmented Federated Learning
|title=Concurrent Entanglement Routing for Quantum Networks: Model and Designs
|speaker=Jianqi
|speaker=Yaliang
}}
|date=2024-04-28}}
{{Latest_seminar
|abstract=Function-as-a-Service (FaaS) is becoming a prevalent paradigm in developing cloud applications. With FaaS, clients can develop applications as serverless functions, leaving the burden of resource management to cloud providers. However, FaaS platforms suffer from the performance degradation caused by the cold starts of serverless functions. Cold starts happen when serverless functions are invoked before they have been loaded into the memory. The problem is unavoidable because the memory in datacenters is typically too limited to hold all serverless functions simultaneously. The latency of cold function invocations will greatly degenerate the performance of FaaS platforms. Currently, FaaS platforms employ various scheduling methods to reduce the occurrences of cold starts. However, they do not consider the ubiquitous dependencies between serverless functions. Observing the potential of using dependencies to mitigate cold starts, we propose Defuse, a Dependency-guided Function Scheduler on FaaS platforms. Specifically, Defuse identifies two types of dependencies between serverless functions, i.e., strong dependencies and weak ones. It uses frequent pattern mining and positive point-wise mutual information to mine such dependencies respectively from function invocation histories. In this way, Defuse constructs a function dependency graph. The connected components (i.e., dependent functions) on the graph can be scheduled to diminish the occurrences of cold starts. We evaluate the effectiveness of Defuse by applying it to an industrial serverless dataset. The experimental results show that Defuse can reduce 22% of memory usage while having a 35% decrease in function cold-start rates compared with the state-of-the-art method.
|confname=ICDCS 2021
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9546470
|title=Defuse: A Dependency-Guided Function Scheduler to Mitigate Cold Starts on FaaS Platforms
|speaker=Linyuanqi
}}
 
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 10:45, 28 April 2024

Time: Friday 10:30-12:00
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [SIGCOMM 2020] Concurrent Entanglement Routing for Quantum Networks: Model and Designs, Yaliang
    Abstract: Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.

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

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