Adaptive Multi-Layer Defense Mechanism for Trusted Federated Learning in Network Security Assessment

The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate. Federated learning offers a promising solution to expedite the training of security assessment models. However, ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge. To address these issues, this study proposes a shard aggregation network structure and a malicious node detection mechanism, along with improvements to the federated learning training process. First, we extract the data features of the participants by using spectral clustering methods combined with a Gaussian kernel function. Then, we introduce a multi-objective decision-making approach that combines data distribution consistency, consensus communication overhead, and consensus result reliability in order to determine the final network sharing scheme. Finally, by integrating the federated learning aggregation process with the malicious node detection mechanism, we improve the traditional decentralized learning process. Our proposed ShardFed algorithm outperforms conventional classification algorithms and state-of-the-art machine learning methods like FedProx and FedCurv in convergence speed, robustness against data interference, and adaptability across multiple scenarios. Experimental results demonstrate that the proposed approach improves model accuracy by up to 2.33% under non-independent and identically distributed data conditions, maintains higher performance with malicious nodes containing poisoned data ratios of 20%–50%, and significantly enhances model resistance to low-quality data.