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A comprehensive survey of federated transfer learning: Challenges, methods and applications

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          🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source
Federated Learning (FL) has gained significant attention as a novel distributed machine learning paradigm that enables collaborative model training while preserving data privacy. However, traditional FL methods face challenges such as data heterogeneity,
The article from MSN, titled "A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods, and Applications," discusses the integration of Federated Learning (FL) and Transfer Learning (TL) into a new paradigm known as Federated Transfer Learning (FTL). FTL aims to leverage the strengths of both FL, which allows for decentralized model training across multiple devices while keeping data localized, and TL, which uses knowledge from one domain to improve learning in another. The survey outlines the challenges faced in FTL, including data heterogeneity, privacy concerns, and communication efficiency. It explores various methods to address these issues, such as personalized federated learning, domain adaptation techniques, and privacy-preserving strategies like differential privacy. The applications of FTL are vast, ranging from healthcare, where it can facilitate cross-institutional learning without sharing sensitive patient data, to IoT devices, enhancing model performance across different environments without centralizing data. The article emphasizes the potential of FTL to revolutionize how machine learning models are developed and deployed in privacy-sensitive and data-scarce scenarios.

Read the Full MSN Article at:
[ https://www.msn.com/en-us/technology/artificial-intelligence/a-comprehensive-survey-of-federated-transfer-learning-challenges-methods-and-applications/ar-AA1wAhvy ]