PEFL: Deep Privacy-Encoding-Based Federated Learning Framework for Smart Agriculture
Prabhat Kumar, Govind P. Gupta, Rakesh Tripathi
Abstract
Smart agriculture (SA) incorporates low-cost and low-energy-consuming sensors and devices to enhance quantitative and qualitative agricultural production. However, this device uses an open communication channel, i.e., Internet, and generates large amount of data in real time and, thus, has the potential to be misused. As a consequence, the major concern in the implementation of SA is minimizing the risk of security and data privacy violation (e.g., adversaries performing inference attacks). To address these challenges, we propose PEFL, a deep privacy-encoding-based federated learning (FL) framework that adopts a perturbation-based encoding and long short-term memory-autoencoder technique to achieve the target of privacy. Then, an FL-based gated recurrent unit neural network algorithm (FedGRU) is designed using the encoded data for intrusion detection. The experimental results based on the ToN-IoT data set reveal that the PEFL can efficiently identify normal and attack patterns after transformation over other non-FL and FL methods.