More information: Wei Guo et al, A comprehensive survey of federated transfer learning: challenges, methods and applications, Frontiers of Computer Science (2024). DOI: 10.1007/s11704-024-40065-x.

3. Federated transfer learning (FTL) uses a pre-trained model on one dataset and fine-tunes it on another, enabling the transfer of insights from one region to another. Here's an outline of the ...

Transfer learning uses models that have been pre-trained on larger datasets. This can save time and resources, as the model doesn't have to be trained from scratch.

Federated learning can help us operationalize this goal. For example, let's say 10 banks use their own teams, with their own models, trained on their own data, to identify illicit transactions. If ...

For companies like Global Retail Corp., the switch to federated learning isn’t just about technology, it’s about finding a more efficient, secure, and effective way to harness the power of AI.

Federated learning is also computationally intensive, which may introduce bandwidth, storage space or computing limitations. While the cloud enables on-demand scalability, cybersecurity teams risk ...

To provide a comprehensive overview of the latest advancements in the field of Federated Transfer Learning (FTL) and offer valuable insights for researchers, a research team led by Fuzhen Zhuang ...