Federated Learning in Big Data with IoT for Intrusion Detection
Anitha Govindaram, J. S. Prasath, A. Suganya, K. Jayasakthi, N. Rajkumar, Jose Anand A. · 2025
Artificial Intelligence (AI) part of Federated Learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has not yet gained mainstream adoption, mostly because of concerns about privacy and security. Security and privacy issues with the FL technique need to be found, examined, and recorded before at this point, we can expect to see significant progress in this field of study or widespread adoption of the FL approach. This article aims to afford a complete review of the methods discussed with FL for huge data by introducing studies from six angles: blockchain, privacy, hybrid approach, VFL, FL, and foundation design and device heterogeneity. This survey can address the challenges of FL, offer their predictions for the future of research on the subject, outline how FL is now being put to use in the real world, and with most salient features.