Resource: Seminar

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Time: 2021-11-05 8:40
Address: Main Building B1-612
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [ICLR 2021] FedMix: Approximation of Mixup under Mean Augmented Federated Learning, Jianqi
    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.
  2. [ICDCS 2021] Defuse: A Dependency-Guided Function Scheduler to Mitigate Cold Starts on FaaS Platforms, Linyuanqi
    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.

History

History

2024

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2022

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2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

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