Entropy-based Client Selection Mechanism for Vehicular Federated Environments

Sousa, J.M.; Lobato, Wellington; Rosário, Denis; Cerqueira, Eduardo; Villas, Leandro A.

doi:10.5753/wperformance.2023.230700

Cited by 3 publications

References 19 publications

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A Robust Client Selection Mechanism for Federated Learning Environments

Veiga,

Sousa,

Morais

et al. 2024

JBCS

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There is a exponential growth of data usage, specially due to the proliferation of connected applications with personalized models for different applications. In this context, Federated Learning (FL) emerges as a promising solution to enable collaborative model training while preserving the privacy and autonomy of participating clients. In a typical FL scenario, clients exhibit significant heterogeneity in terms of data distribution and hardware configurations. In this way, randomly sampling clients in each training round may not fully exploit the local updates from heterogeneous clients, resulting in lower model accuracy, slower convergence rate, degraded fairness, etc. In addition, malicious users could disseminate incorrect weights, which may decrease the accuracy of aggregated models and increase the time for convergence in FL. In this article, we introduce Resilience-aware Client Selection Mechanism for non-IID data and malicious clients in FL environment, called RICA. The proposed mechanism employs data size and entropy as criteria for client selection. In addition, RICA relies Centroid-Based Kernel Alignment (CKA) to identify and exclude potentially malicious clients. Our evaluation shows an improvement of 125% in Accuracy values in a scenario of malicious clients, which means the RICA+CKA demonstrates a more stable and resilient approach, reaching 90% accuracy in a few rounds compared to the default average approach, reached only around 30%. Therefore, results of the behavior of RICA+CKA in different datasets show the evaluation of different numbers of clients reaching around 90% while the other approach does not pass the 50% Accuracy.

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A Robust Client Selection Mechanism for Federated Learning Environments

Veiga,

Sousa,

Morais

et al. 2024

JBCS

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MESFLA: Model Efficiency through Selective Federated Learning Algorithm

Barros,

Veiga,

Morais

et al. 2024

JISA

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Integrating big data and deep learning across various applications has significantly enhanced intelligence and efficiency in our daily lives. However, it also requires extensive data sharing, raising significant communication and privacy concerns. In this context, Federated Learning (FL) emerges as a promising solution to enable collaborative model training while preserving the privacy and autonomy of participating clients. FL facilitates collaborative model training by enabling data to be trained locally on devices, eliminating the need for individual information sharing among clients. A client selection mechanism strategically chooses a subset of participating clients to contribute to the model training in each learning round. However, an efficient selection of clients to participate in the training process directly impacts model convergence/accuracy and the overall communication load on the network. In addition, FL faces challenges when dealing with non-Independent and Non-Identically Distributed (non-IID) data, where the diversity in data distribution often leads to reduced classification accuracy. Hence, designing an efficient client selection mechanism in a scenario with non-IID data is essential, but it is still an open issue. This article proposes a Model Efficiency through Selective Federated Learning Algorithm called MESFLA. The mechanism employs a Centered Kernel Alignment (CKA) algorithm to search for similar models based on data weight or similarity between models, i.e., grouping participants with comparable data distributions or learning objectives. Afterward, MESFLA selects the clients with more relevance in each group based on data weight and entropy. Our comprehensive evaluation across multiple datasets, including MNIST, CIFAR-10, and CIFAR-100, demonstrates MESFLA's superior performance over traditional FL algorithms. Our results show an accuracy improvement and a minor loss in each client aggregation of the new global model sent to clients with a difference of 3 rounds using the Data Weight in comparison with the other selection methods.

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Evaluation of Client Selection Mechanisms in Vehicular Federated Learning Environments with Client Failures

Sousa,

Ribeiro,

Bastos

et al. 2024

Anais Do XLII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2024)

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Federated Learning (FL) emerges as a promising solution to enable collaborative model training for autonomous vehicles while preserving privacy and communication overhead issues. An efficient selection of clients to participate in the training process is still challenging, especially in scenarios with statistical heterogeneity of data distribution and client failure events. Client failure is an uncontrollable event in the training process that reduces accuracy, convergence, and speed. Therefore, investigating the performance of client selection mechanisms in this scenario is crucial. This paper presents a reliability and robustness analysis of entropy-based client selection mechanisms in FL environments with client failure. The results demonstrated that entropy-based selection outperformed the other methods regarding training loss, accuracy, and AUC, particularly in high client dropout scenarios. These findings show the importance of considering entropy data for client selection when addressing the challenges posed by client failure in FL scenarios.

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Entropy-based Client Selection Mechanism for Vehicular Federated Environments

Cited by 3 publications

References 19 publications

A Robust Client Selection Mechanism for Federated Learning Environments

A Robust Client Selection Mechanism for Federated Learning Environments

MESFLA: Model Efficiency through Selective Federated Learning Algorithm

Evaluation of Client Selection Mechanisms in Vehicular Federated Learning Environments with Client Failures

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