TY - GEN
T1 - Federated Learning Model with Augmentation and Samples Exchange Mechanism
AU - Połap, Dawid
AU - Srivastava, Gautam
AU - Lin, Jerry Chun Wei
AU - Woźniak, Marcin
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The use of intelligent solutions often comes down to the use of already trained classifiers, which is caused by one of their biggest drawbacks. It is the accuracy or effectiveness of artificial intelligence methods, which are algorithms called data-hungry. It means that it depends on the number of samples in the database, and the quality of the classifier could be better if their number is high and the samples are different. In this paper, we propose a solution based on the idea of federated learning in an application for intelligent systems. The proposed solution consists not only in the division of the database among workers but also in the quality of the samples and their possible exchange. Exchanging samples for a particular worker means labeling difficult to classify samples. These samples are used to expand the sets using the generative adversarial network. The mathematical model of a proposal is described, then the experimental results are shown and discussed with the comparison to the classic approach.
AB - The use of intelligent solutions often comes down to the use of already trained classifiers, which is caused by one of their biggest drawbacks. It is the accuracy or effectiveness of artificial intelligence methods, which are algorithms called data-hungry. It means that it depends on the number of samples in the database, and the quality of the classifier could be better if their number is high and the samples are different. In this paper, we propose a solution based on the idea of federated learning in an application for intelligent systems. The proposed solution consists not only in the division of the database among workers but also in the quality of the samples and their possible exchange. Exchanging samples for a particular worker means labeling difficult to classify samples. These samples are used to expand the sets using the generative adversarial network. The mathematical model of a proposal is described, then the experimental results are shown and discussed with the comparison to the classic approach.
KW - Artificial intelligence
KW - Convolutional neural network
KW - Federated learning
KW - Generative adversarial network
KW - Internet of Things
UR - https://www.scopus.com/pages/publications/85117450500
U2 - 10.1007/978-3-030-87986-0_19
DO - 10.1007/978-3-030-87986-0_19
M3 - Conference contribution
AN - SCOPUS:85117450500
SN - 9783030879853
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 214
EP - 223
BT - Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings
A2 - Rutkowski, Leszek
A2 - Scherer, Rafał
A2 - Korytkowski, Marcin
A2 - Pedrycz, Witold
A2 - Tadeusiewicz, Ryszard
A2 - Zurada, Jacek M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021
Y2 - 21 June 2021 through 23 June 2021
ER -