A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications

arXiv:2403.01387v1 Announce Type: new
Abstract: Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant. Therefore, many FL methods do not work well due to the training and test data of each participant may not be sampled …

abstract applications arxiv challenges cs.dc cs.lg data data sharing distributed federated learning global machine machine learning multiple novel paradigm practice preservation privacy survey train transfer transfer learning type