Meta-heuristic as manager in federated learning approaches for image processing purposes
Dawid Połap, Marcin Woźniak
Abstract
The new form of artificial intelligence training, i.e. federated learning, is becoming more popular in the last few years. It is an optimization problem that includes additional mechanisms such as aggregation and data transmission. In this paper, we propose a hybridization of this type of training with a meta-heuristic. The meta-heuristic algorithm is adapted to manage the entire process as well as to analyze the best models to minimize attacks on this type of collaboration. The proposed solution is based on minimizing the general model error, with additional control mechanisms for incoming models, or adapting the aggregation method depending on the quality of the model. The innovative solution has been analyzed in terms of its application to the problem of image classification using classical and convolutional neural networks, and the most popular meta-heuristic algorithms. The proposal was analyzed in terms of the accuracy of the general model as well as for security against poisoning attacks. We reached 91% of accuracy using the proposed method with the Red Fox Optimization Algorithm and 95% in terms of detection of poisoned samples in the database.