Robust and Communication-Efficient Federated Domain Adaptation via Random Features

Modern machine learning (ML) models have grown to a scale where training them on a single machine becomes impractical. As a result, there is a growing trend to leverage federated learning (FL) techniques to train large ML models in a distributed and collaborative manner. These models, however, when...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering 2025-03, Vol.37 (3), p.1-14
Hauptverfasser:	Feng, Zhanbo, Wang, Yuanjie, Li, Jie, Yang, Fan, Lou, Jiong, Mi, Tiebin, Qiu, Robert. C., Liao, Zhenyu
Format:	Artikel
Sprache:	eng
Schlagworte:	Classification tree analysis Data models Feature extraction federated domain adaptation Federated learning Kernel kernel method maximum mean discrepancy Protocols Random features Reliability Training Transfer learning Vectors
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