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Efficient Task Scheduling for Federated Learning-Driven Image Recognition in Cloud-Edge Collaborative Environments

Published: 28 February 2024 Publication History

Abstract

Cloud-edge collaboration and artificial intelligence integration have advanced federated learning image recognition model training. However, in cloud-edge collaborative federated learning, imbalanced sample data often leads to low global model aggregation accuracy. To address this, a cloud-edge collaborative federated learning model aggregation method for sample imbalance is proposed, which considers local model accuracy, stability, and sample size as evaluation weights to address the issue of uneven sample distribution affecting global model aggregation. By increasing the contribution weight of high-quality local models, the impact of sample imbalance and low-quality models on the global model is reduced. This method is implemented through plugins and compared with FedAvg model aggregation method in multiple imbalanced sample scenarios. The experimental results demonstrate that this method mitigates the impact of sample diversity on global model aggregation and improves accuracy in imbalanced-sampling scenarios.

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  1. Efficient Task Scheduling for Federated Learning-Driven Image Recognition in Cloud-Edge Collaborative Environments

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      ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
      October 2023
      589 pages
      ISBN:9798400707988
      DOI:10.1145/3633637
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

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      Published: 28 February 2024

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      • Shandong Provincial Natural Science Foundation?Shandong Innovation Ability Improvement Project of Science and Technology small and medium-sized enterprises

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