
This paper introduces a novel regret-optimal algorithm for federated transfer learning with kernel regression, demonstrating its theoretical advantages and practical application in American option pricing.


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regret-optimal-federated-transfer-learning-for-kernel-regression-theory-and-application-in-american-option-pricing


Regret-Optimal Federated Transfer Learning for Kernel Regression: Theory and Application in American Option Pricing


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The author explores the challenges and solutions in federated transfer learning by integrating transfer learning into federated learning. They address issues such as data heterogeneity, system heterogeneity, incremental data, labeled data scarcity, and model heterogeneity.


A-Comprehensive-Survey-of-Federated-Transfer-Learning-Challenges-Methods-and-Applications


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