Abstract
Federated learning aims at building machine learning models without compromising data privacy from the clients. Since different clients naturally have different data distributions (i.e., the non-i.i.d. issue), it is intuitive to embed transfer learning technology into the federated learning system. In this chapter, we first introduce a healthcare task and show how to prepare data for federated learning.
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Wang, J., Chen, Y. (2023). Federated Learning for Personalized Healthcare. In: Introduction to Transfer Learning. Machine Learning: Foundations, Methodologies, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-19-7584-4_19
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DOI: https://doi.org/10.1007/978-981-19-7584-4_19
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