Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization
Federated learning uses the concept of decentralized training of n number of local clients for a small number of epochs say 2-5, and then averaging the learned weights of all local clients, and evaluating on test dataset with the average weights loaded to a global model. The train dataset is split i...
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IEEE
2024-01-01
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| author | Mohit Agarwal Garima Jaiswal Rohit Kumar Kaliyar Akansha Singh Krishna Kant Singh S. S. Askar Mohamed Abouhawwash |
| author_facet | Mohit Agarwal Garima Jaiswal Rohit Kumar Kaliyar Akansha Singh Krishna Kant Singh S. S. Askar Mohamed Abouhawwash |
| author_sort | Mohit Agarwal |
| collection | DOAJ |
| description | Federated learning uses the concept of decentralized training of n number of local clients for a small number of epochs say 2-5, and then averaging the learned weights of all local clients, and evaluating on test dataset with the average weights loaded to a global model. The train dataset is split into n clusters and each cluster acts as a distributed data for each local model. Each round of weight averaging and then uploading the average weights on each local client for further training is called communication round and it was observed that similar accuracy can be obtained with a lesser amount of training time. In this paper, instead of averaging the weights, a weighted mean concept was developed where the PSO vector helps to find the weight values for the best accuracy of a global model. It was found that PSO can help in two ways by bettering the accuracy and also reducing the training time. The proposed approach can enhance the performance of pre-trained models like AlexNet, VGG16, InceptionV3, and ResNet50 on CIFAR-10 and CIFAR-100 datasets. The maximum increase was found with VGG16 of around 26.01% for CIFAR-10 and 26.84% for CIFAR-100. Similarly, on the Tomato dataset, AlexNet accuracy can be increased by 28.56%. Multi-modal model accuracy on the fake news dataset was also enhanced by 8.21%. |
| format | Article |
| id | doaj-art-ca53c90719244b7ba72f1ce0117bbbe9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ca53c90719244b7ba72f1ce0117bbbe92025-08-20T02:12:41ZengIEEEIEEE Access2169-35362024-01-011216196816198110.1109/ACCESS.2024.348510010731716Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm OptimizationMohit Agarwal0https://orcid.org/0000-0001-7216-3886Garima Jaiswal1https://orcid.org/0000-0002-2676-299XRohit Kumar Kaliyar2https://orcid.org/0000-0001-7233-872XAkansha Singh3https://orcid.org/0000-0002-5520-8066Krishna Kant Singh4https://orcid.org/0000-0002-6510-6768S. S. Askar5https://orcid.org/0000-0002-1167-2430Mohamed Abouhawwash6https://orcid.org/0000-0003-2846-4707School of CSET, Bennett University, Greater Noida, IndiaSchool of CSET, Bennett University, Greater Noida, IndiaSchool of CSET, Bennett University, Greater Noida, IndiaSchool of CSET, Bennett University, Greater Noida, IndiaDelhi Technical Campus, Greater Noida, IndiaDepartment of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Mathematics, Faculty of Science, Mansoura University, Mansoura, EgyptFederated learning uses the concept of decentralized training of n number of local clients for a small number of epochs say 2-5, and then averaging the learned weights of all local clients, and evaluating on test dataset with the average weights loaded to a global model. The train dataset is split into n clusters and each cluster acts as a distributed data for each local model. Each round of weight averaging and then uploading the average weights on each local client for further training is called communication round and it was observed that similar accuracy can be obtained with a lesser amount of training time. In this paper, instead of averaging the weights, a weighted mean concept was developed where the PSO vector helps to find the weight values for the best accuracy of a global model. It was found that PSO can help in two ways by bettering the accuracy and also reducing the training time. The proposed approach can enhance the performance of pre-trained models like AlexNet, VGG16, InceptionV3, and ResNet50 on CIFAR-10 and CIFAR-100 datasets. The maximum increase was found with VGG16 of around 26.01% for CIFAR-10 and 26.84% for CIFAR-100. Similarly, on the Tomato dataset, AlexNet accuracy can be increased by 28.56%. Multi-modal model accuracy on the fake news dataset was also enhanced by 8.21%.https://ieeexplore.ieee.org/document/10731716/Federated learningparticle swarm optimizationoptimizationmodel performancemultimodal |
| spellingShingle | Mohit Agarwal Garima Jaiswal Rohit Kumar Kaliyar Akansha Singh Krishna Kant Singh S. S. Askar Mohamed Abouhawwash Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization IEEE Access Federated learning particle swarm optimization optimization model performance multimodal |
| title | Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization |
| title_full | Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization |
| title_fullStr | Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization |
| title_full_unstemmed | Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization |
| title_short | Federated Learning With Dataset Splitting and Weighted Mean Using Particle Swarm Optimization |
| title_sort | federated learning with dataset splitting and weighted mean using particle swarm optimization |
| topic | Federated learning particle swarm optimization optimization model performance multimodal |
| url | https://ieeexplore.ieee.org/document/10731716/ |
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