A Behavioral Recognition-Based Federated Learning Framework for IoT Environments
Ruizhong Du, Shuai Li, Pengyuan Zhao · 2024
Identifying the behavior and intent of various Internet of Things (IoT) devices distributed across different environments is a challenge. The previous centralized modeling approach ran the risk of invading privacy. This study develops a federated learning approach enhanced by Long short-term memory (LSTM) networks to improve the accuracy of modeling the behavior of iot devices in decentralized networks. We extract periodic communication features locally at each node, focusing on relevant behavior sequences while protecting privacy. Applying LSTM to these periodic sequences captures the temporal dynamics necessary for behavioral modeling. The federated LSTM model then aggregates the locally learned behavior patterns to classify device sequences and interactions without sharing the raw data. Experiments show that this method can accurately identify the key behaviors of iot devices. By focusing on periodic communication, our technology enables collaborative device behavior identification across distributed nodes without compromising user privacy. This provides a pathway for privacy-protecting behavioral intelligent federated learning in iot environments.