rFedFW: Secure and trustable aggregation scheme for Byzantine-robust federated learning in Internet of Things

Federated learning is a promising approach in the Internet of Things (IoT) that enables collaborative and distributed machine learning among massive IoT devices without sharing private data, thereby constructing smart IoT applications. However, traditional federated learning approaches are unable to monitor the local training process of devices. Some malicious devices can exploit the vulnerability to conduct Byzantine attacks, which may potentially lead to the failure or compromise of shared global model. In this paper, we propose a new Byzantine-robust federated learning framework called rFedFW, which aims to achieve secure and trustable federated learning in the IoT. Specifically, we propose a dual filtering mechanism to identify and discard malicious gradients. Furthermore, we design an adaptive weight adjustment scheme that dynamically reduces the aggregated weight of potentially malicious gradients, ultimately achieving robust model aggregation. Additionally, we propose a dynamic clipping method to reduce the magnitude of various gradients, and we incorporate an additive model aggregation scheme with momentum to smooth the effects of local gradients and achieve efficient model aggregation. Extensive experimental results on various datasets demonstrate the effectiveness and robustness of rFedFW.