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

Shuang Dai, Fanlin Meng

Research output: Contribution to journalArticlepeer-review

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 explores OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are also 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.

Original languageEnglish
JournalApplied Intelligence
DOIs
Publication statusPublished - 30 Aug 2022

Keywords

  • Federated transfer learning
  • Online federated learning
  • Online learning
  • Online transfer learning
  • Privacy-preserving

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