Lincong Zhao1, Liandong Chen1, Peipei Shen1, Zizhou Liu1, Chengzhu Li1, Fanqin Zhou2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5057-5071, 2025, DOI:10.32604/cmc.2025.067521
- 23 October 2025
Abstract 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… More >
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