Federated Learning (FL) has emerged as a key enabler for privacy-preserving, decentralized sensing systems, giving rise to Federated Mobile Crowdsensing (F-MCS). A well-known bottleneck in such systems is the inefficiency of synchronous training, which stalls for all participants and is susceptible to stragglers in het...
Xiao-Di Wang, Yun-Wei Dong, Wei-Zhi Meng et al.· IEEE Transactions on Network...· 1 citation
Distributed Learning (DL) is a fundamental paradigm for large-scale model training in mobile and edge computing. However, its practical adoption is often plagued by performance degradation due to straggler and Byzantine nodes, compromising overall robustness and efficiency. Although coded computing provides theoretical...
Ling-Ling Wang, Linqing Xu, Yufei Liu et al.· IEEE Transactions on Informa...· 0 citations
Decision trees are widely used in various domains, such as user behavior analysis and financial risk assessment. Recently, increasing concerns about data privacy have driven the development of secure decision tree training frameworks. We propose ESecDT, a novel framework that enables collaborative decision tree trainin...
Jingchen Zhao, Kaping Xue, Meng Li et al.· IEEE Transactions on Informa...· 0 citations
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