MultiFed: A fast converging federated learning framework for services QoS prediction via cloud–edge collaboration mechanism

Jian, Lin; Li, Yusen; Xu, Zhuo

doi:10.1016/j.knosys.2023.110463

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…It takes the different needs of the clients into account in order to achieve personalization. The multi-model approach includes two strategies: 1) Clients are segmented into multiple groups [9]. For each group, the server maintains an aggregate model and performing model optimization tailored to group members.…”

Section: B: Methods Based On Model Framework Adjustmentmentioning

confidence: 99%

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pFedLN: Personalized Federated Learning Framework With Layer-Wised and Neighbor-Based Aggregation for QoS Prediction

She,

Xu,

Jin

et al. 2024

IEEE Access

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In the era of a more advanced and intelligent Internet, the highly sophisticated service-oriented internet provides users with a diverse array of similar services. Accurate Quality of Service (QoS) prediction plays a pivotal role in helping users choose the optimal service from a multitude of available options. Traditional federated learning models offer a secure method for multiple clients to collaborate on QoS predictions. However, these models still employ a uniform approach that overlooks the unique requirements of individual clients. In order to meet the different needs of a wide range of customers for models, we propose an innovative personalized federated learning framework with layer-wised and neighbor-based aggregation for QoS prediction (pFedLN). In the proposed framework, we consider the privacy and functional disparities among layers in neural network models and employ diverse aggregation strategies for layers serving different functions. In addition, the similarity between neighbors will be taken into account during the aggregation process. This results in the creation of personalized models for each client that better align with their specific requirements. Sufficient experiments are conducted on a real-world dataset and the results indicate that our approach have a clear advantage in improving the effectiveness of personalization compared to existing approaches.INDEX TERMS Internet of Service, personalized federated learning, QoS prediction.

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Section: B: Methods Based On Model Framework Adjustmentmentioning

confidence: 99%

“…In non-IID environments, the distribution of data among participants can vary significantly. As a result, the average aggregation method, which assumes equal contributions from all local models, becomes inapplicable, and its use may result in a degradation of model performance [9].…”

Section: Introductionmentioning

confidence: 99%

pFedLN: Personalized Federated Learning Framework With Layer-Wised and Neighbor-Based Aggregation for QoS Prediction

She,

Xu,

Jin

et al. 2024

IEEE Access

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“…And Liu et al [29]introduced a method to effectively use the similarity between models for model training and prediction under the federated model framework. Considering heterogeneous local QoS datasets, Xu et al [30] proposed a federated learning framework which divides the client into multiple regions for model aggregation and reduces the time of model convergence.…”

Section: Related Workmentioning

confidence: 99%

LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

Cai,

Liu,

et al. 2024

Preprint

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As a distributed training method, federated learning (FL) has been widely used in the field of quality-of-service (QoS) prediction. However, existing FL-based QoS prediction methods ignore the unreliability of end devices, which will lead to wasted training resources and high communication costs. Considering that the instability of end devices in real training environments, we propose a low cost semi-asynchronous federated learning method (LCSA-Fed) based on lag tolerance to overcome the lower convergence rate and suboptimal prediction accuracy of models. LCSA-Fed is able to reduce model communication costs and training costs by tolerating relatively lagging local models. At the same time, we employ innovations in both the user selection phase and the model aggregation phase to improve prediction accuracy while reducing overheads. By conducting relevant validation experiments on a publicly available QoS dataset, we conclude that our model LCSA-Fed can effectively reduce overhead and improve prediction accuracy.

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Energy–latency tradeoffs edge server selection and DQN-based resource allocation schemes in MEC

Zhang

Liu

et al. 2023

Wireless Netw

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MultiFed: A fast converging federated learning framework for services QoS prediction via cloud–edge collaboration mechanism

Cited by 7 publications

References 22 publications

pFedLN: Personalized Federated Learning Framework With Layer-Wised and Neighbor-Based Aggregation for QoS Prediction

pFedLN: Personalized Federated Learning Framework With Layer-Wised and Neighbor-Based Aggregation for QoS Prediction

LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

Energy–latency tradeoffs edge server selection and DQN-based resource allocation schemes in MEC

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