Digestive neural networks: A novel defense strategy against inference attacks in federated learning

Federated Learning (FL) is an efficient and secure machine learning technique designed for decentralized computing systems such as fog edge computing. Its process employs frequent communications the participating local devices send updates, either gradients or parameters of their models, to a central server that aggregates them redistributes new weights devices. In FL, private data does not leave individual devices, thus, rendered robust solution in terms privacy preservation. However, recently introduced membership inference attacks pose critical threat impeccability FL mechanisms. By eavesdropping only on updates transferring center server, these can recover device. A prevalent against differential scheme augments sufficient amount noise each update hinder recovering process. it suffers from significant sacrifice classification accuracy FL. To effectively alleviate problem, this paper proposes Digestive Neural Network (DNN), independent neural network attached The owned by device will pass through DNN then train modifies input data, which results distorting way maximize while minimized. Our simulation result shows proposed performance both gradient sharing- weight sharing-based For sharing, achieved higher 16.17% 9% lower attack than existing schemes. sharing scheme, at most 46.68% success rate with 3% accuracy.