FedLesScan: Mitigating Stragglers in Serverless Federated Learning

Elzohairy, Mohamed Mansour; Chadha, Mohak; Jindal, Anshul; Grafberger, Andreas; Gu, Jianfeng; Gerndt, Michael; Abboud, Osama

doi:10.1109/bigdata55660.2022.10021037

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Function-as-a-Service (FaaS) is the computational concept of serverless computing and has gained significant popularity and widespread adoption in various application domains such as machine learning [25,28,31,32], edge computing [47], and heterogeneous computing [33,34,36]. In FaaS, developers implement fine-grained pieces of code called functions that are packaged independently in containers and uploaded to a FaaS platform.…”

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confidence: 99%

Migrating from microservices to serverless

Chadha

Pacyna

Jindal

et al. 2022

Proceedings of the Eighth International Workshop on Serverless Computing

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This paper presents GreenCourier, a novel scheduling framework that enables the runtime scheduling of serverless functions across geographically distributed regions based on their carbon efficiencies. Our framework incorporates an intelligent scheduling strategy for Kubernetes and supports Knative as the serverless platform. To obtain real-time carbon information for different geographical regions, our framework supports multiple marginal carbon emissions sources such as WattTime and the Carbon-aware SDK. We comprehensively evaluate the performance of our framework using the Google Kubernetes Engine and production serverless function traces for scheduling functions across Spain, France, Belgium, and the Netherlands. Results from our experiments show that compared to other approaches, GreenCourier reduces carbon emissions per function invocation by an average of 13.25%. CCS CONCEPTS• Computer systems organization → Cloud computing; • Social and professional topics → Sustainability.

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mentioning

confidence: 99%

Migrating from microservices to serverless

Chadha

Pacyna

Jindal

et al. 2022

Proceedings of the Eighth International Workshop on Serverless Computing

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Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

Waqas Khan,

Park,

Ahmad

et al. 2024

IET Collab Intel Manufact

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Decentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short‐term memory model with real‐time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.

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Efficient Federated Learning via Joint Communication and Computation Optimization

Wang,

Zhao,

et al. 2024

IEEE Trans. Veh. Technol.

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FedLesScan: Mitigating Stragglers in Serverless Federated Learning

Cited by 4 publications

References 14 publications

Migrating from microservices to serverless

Migrating from microservices to serverless

Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

Efficient Federated Learning via Joint Communication and Computation Optimization

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