Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning

2022/02/07 by Shuang Dai, Dai, Shuang, Fanlin Meng +1 · 1 citation
Computer Science · Health Professions · #Artificial Intelligence in Healthcare #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data

paper · doi:10.48550/arxiv.2202.03070

openalex publication_date 2022/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explored OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Besides, practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are highlighted in this work. Furthermore, this survey provides insight into potential future research areas and aims to serve as a resource for professionals developing online federated and transfer learning frameworks.

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• Enhancing Privacy Preservation and Reducing Analysis Time with Federated Transfer Learning in Digital Twins-based Computed Tomography Scan Analysis

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