Anomaly detection and defense techniques in federated learning: a comprehensive review
Chang Zhang,
Shunkun Yang,
Lingfeng Mao
et al.
Abstract:In recent years, deep learning methods based on a large amount of data have achieved substantial success in numerous fields. However, with increases in regulations for protecting private user data, access to such data has become restricted. To overcome this limitation, federated learning (FL) has been widely utilized for training deep learning models without centralizing data. However, the inaccessibility of FL data and heterogeneity of the client data render difficulty in providing security and protecting the… Show more
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