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Mar, 2021
走向个性化联邦学习
Towards Personalized Federated Learning
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Alysa Ziying Tan, Han Yu, Lizhen Cui, Qiang Yang
TL;DR
本综述论文研究了个性化联邦学习（PFL）的领域，着重解决异构数据带来的基本问题，通过分类和分析PFL技术，提出其关键挑战和机会，并展望了未来研究的发展方向。
Abstract
As
artificial intelligence
(AI)-empowered applications become widespread, there is growing awareness and concern for user privacy and data confidentiality. This has contributed to the popularity of
federated learning
→