Federated Learning
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Twitter thread summarize with ChatGPT.icon
Federated learning is a technique for training machine learning models without moving large amounts of data to a central server Instead, copies of the model are sent to the devices where the data resides, and the model is trained locally on each device
https://gyazo.com/afa38dbf8d8fac23d5fb471895b4ccd2
The updated models are then sent back to a central server, where they are aggregated to improve the global model without revealing any private data.
It is used for applications such as improving word recommendation on Android keyboards and voice recognition on Siri.
Demerit
The cost for implementing federated learning is higher than collecting the information and processing it centrally, especially during the early phases of R&D when the training method and process are still being iterated on
So the concept of importing something that is considered to some extent to be the default (Common-Sense) and then using FL to fine-tune it is likelytkgshn.icon*2