Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission

Prasetyo, Heri Prasetyo Heri; Prasetyo, Alim Wicaksono Hari Prayuda Heri; Prayuda, Chih-Hsien Hsia Alim Wicaksono Hari; Hsia, Muhammad Alif Wisnu Chih-Hsien

doi:10.53106/160792642022052303005

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The challenge of effectively training deep-learning models while preserving client data privacy has emerged as a critical issue. Therefore, customer data privacy has become a major concern in deep-learning technology [4,5].…”

Section: Introductionmentioning

confidence: 99%

Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets

Lai,

Chen,

Chou

et al. 2024

Future Internet

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Federated learning trains a neural network model using the client’s data to maintain the benefits of centralized model training while maintaining their privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client’s data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments.

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Section: Introductionmentioning

confidence: 99%

Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets

Lai,

Chen,

Chou

et al. 2024

Future Internet

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An Image Adaptive Rate Mechanism in Semantic Communication for Image Endogenous Semantics

Shi,

Li,

Gao

et al. 2024

IEEE Trans. Veh. Technol.

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Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in non-IID Datasets

Lai,

Chen,

Chou

et al. 2024

Preprint

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Federated learning trains a neural network model using the client's data to maintain the benefits of centralized model training, while maintaining the privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client's data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments.

show abstract

Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission

Cited by 3 publications

References 11 publications

Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets

Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets

An Image Adaptive Rate Mechanism in Semantic Communication for Image Endogenous Semantics

Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in non-IID Datasets

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