Efficient and Secure Federated Knowledge Transfer Under Non-IID Settings in IoT

In the era of Internet of Things (IoT) and federated learning (FL), where distributed training models are essential, the FL paradigm has come into the spotlight for researchers. However, the inconsistency in the sources of client data and non-independent and identically distributed (Non-IID) heterogeneous characteristics lead to loss in model accuracy. Existing method which attempts to homogenize data distribution among clients based on generative adversarial networks (GANs) incurs high computation overheads on clients in IoT. In this article, we propose a lightweight feature prototype knowledge transfer (FPKT) mechanism. By capturing the essence of data categories, FPKT generates pseudo-features without requiring the original data features, thereby efficiently enhancing model accuracy. We formally prove that FPKT resists chosen plaintext attack (CPA) and experiments demonstrate that our scheme achieves a hundredfold increase in computational efficiency and improves model accuracy by up to 40%.