Deep learning based federated learning scheme for decentralized blockchain
Blockchain; CNN model; Deep Learning; Federated Learning; LSTM model
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors.
Ramkumar G.; Sivakumar S.; Soni M.; Muhammed Y.; Salman H.M.; Soomar A.M.
Artificial Intelligence, Blockchain, Computing and Security: Volume 1, Vol-1, pp. 679-689.
Ramkumar G., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Sivakumar S., Department of Electrical and Electronics Engineering, St. Josephs College of Engineering, OMR, Chennai, India; Soni M., Department of CSE, University Centre for Research & Development, Chandigarh University, Punjab, Mohali, India; Muhammed Y., College of Technical Engineering, Al-Farahidi University, Baghdad, Iraq; Salman H.M., Al-Turath Universiy College, Baghdad, Iraq; Soomar A.M., Faculty of Electrical and Control Engineering, Gda?sk University of Technology, Poland