A Lightweight Integrity-Driven Federated Learning Approach to Mitigate Poisoning Attacks in IoT

Despite its distributed nature and being privacy-preserving by nature, Federated Learning (FL) is vulnerable to poisoning attacks in which malicious actors can inject fake model parameters or false data to compromise the learning process. This article introduces a lightweight and efficient integrity verification scheme to mitigate these attacks on FL platforms in Internet of Things (IoT) networks. The core design of the proposed scheme is based on a customized feed-forward neural network (FFNN) integrated with a fast, secure, and efficient Keccak512 hashing algorithm. This combination balances security, speed, efficiency, and suitability for resource-constrained IoT devices. The proposed model was trained and evaluated using the real-time IDSIoT2024 dataset, and the results demonstrated a higher classification accuracy of 98.29% with a lower memory footprint of 87.58KB. Furthermore, the lower computation and communication overheads, low CPU and GPU memory utilization confirm the resource and time efficiency of the proposed scheme to effectively mitigate the poisoning attacks in FL architectures.