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View all- Liu WWang YLi KTian ZShe W(2025)Ftmoe: a federated transfer model based on mixture-of-experts for heterogeneous image classificationCluster Computing10.1007/s10586-024-04759-y28:3Online publication date: 1-Jun-2025
In recent years, data privacy preservation has received increased attention in artificial intelligence. Federated learning, as a paradigm for privacy-preserving machine learning, can considerably reduce the risk of privacy leakage by training ...
Recently, Federated Learning (FL) has been applied in various research domains specially because of its privacy preserving and decentralized approach of model training. However, very few FL applications have been developed for the Radio Access Network (...
Federated learning (FL) is a promising privacy-preserving machine learning paradigm over distributed located data. In FL, the data is kept locally by each user. This protects the user privacy, but also makes the server difficult to verify data quality, ...
IEEE Press
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