FL has attracted significant attention recently, and emerged as a distributed deep learning paradigm. With FL, each user device trains its local model with its private data to generate local updates sent to the edge server without sharing the device’s private data. The edge server then aggregates the local updates to train a global model, which is sent back to the user devices for the next round of FL training. Based on FL, individual data privacy is protected as no private data is shared [28].