Author

Honglong Chen

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#edge computing Open access Sep 2026

Robust Client–Server Watermarking for Split Federated Learning

Split federated learning (SFL) is renowned for its low computational overhead, extremely suitable for resource-constrained edge computing scenarios while inheriting privacy-preserving characteristics from federated learning (FL). In this framework, clients employ lightweight models to process private data locally and t...

Jia-Xiong Tang, Zhengchunmin Dai, Liantao Wu et al. · IEEE Internet of Things Jour... · 0 citations

#federated learning Oct 2026

Defending Poisoning Attacks in Federated Learning Under System and Data Heterogeneity

Federated learning (FL) is susceptible to poisoning attacks, where malicious clients manipulate local data or models to disrupt training. The system and data heterogeneity inherent in practical FL systems exacerbates these vulnerabilities, rendering existing defense mechanisms ineffective or infeasible. Specifically, d...

Peng Sun, Tao Liu, Yang Xu et al. · IEEE Transactions on Mobile... · 0 citations