Transfer learning vs federated learning: A comparative analysis

Federated learning enables smarter models, lower latency, and less power consumption, all while ensuring privacy.
Think of the things artificial intelligence and machine learning have accomplished in the last few years-- real-time translations, outperforming humans at board games, drug discovery etc. Transfer learning, federated learning, reinforcement learning, self-supervised learning etc., are the cutting-edge techniques that made these milestones possible. While transfer learning is an old machine learning technique, federated learning was introduced in 2017 by Google. Transfer learning Deep learning models need huge swathes of labelled data to be trained on to learn and work effectively. The process is also time-consuming. Transfer learning can help tackle these challenges. Suppose you have good knowledge in a certain topic; learning allied topics becomes easier as you can always build o
Subscribe or log in to Continue Reading

Uncompromising innovation. Timeless influence. Your support powers the future of independent tech journalism.

Already have an account? Sign In.

📣 Want to advertise in AIM? Book here

Picture of Sreejani Bhattacharyya
Sreejani Bhattacharyya
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good. Reach me at sreejani.bhattacharyya@analyticsindiamag.com
Related Posts
AIM Print and TV
Don’t Miss the Next Big Shift in AI.
Get one year subscription for ₹5999
Download the easiest way to
stay informed