✦Increased online sales by 25% in the first 6 months✦Positive feedback from users✦Design first approach✦Blazing fast performance✦Highly scalable architecture✦Aesthetic UI✦Intuitive UX✦Highly secure✦Easy to use✦Easy to maintain✦Tested thoroughly✦Highly performant✦Realtime updates✦Increased online sales by 25% in the first 6 months✦Positive feedback from users✦Design first approach✦Blazing fast performance✦Highly scalable architecture✦Aesthetic UI✦Intuitive UX✦Highly secure✦Easy to use✦Easy to maintain✦Tested thoroughly✦Highly performant✦Realtime updates
✦Increased online sales by 25% in the first 6 months✦Positive feedback from users✦Design first approach✦Blazing fast performance✦Highly scalable architecture✦Aesthetic UI✦Intuitive UX✦Highly secure✦Easy to use✦Easy to maintain✦Tested thoroughly✦Highly performant✦Realtime updates✦Increased online sales by 25% in the first 6 months✦Positive feedback from users✦Design first approach✦Blazing fast performance✦Highly scalable architecture✦Aesthetic UI✦Intuitive UX✦Highly secure✦Easy to use✦Easy to maintain✦Tested thoroughly✦Highly performant✦Realtime updates
✦Increased online sales by 25% in the first 6 months✦Positive feedback from users✦Design first approach✦Blazing fast performance✦Highly scalable architecture✦Aesthetic UI✦Intuitive UX✦Highly secure✦Easy to use✦Easy to maintain✦Tested thoroughly✦Highly performant✦Realtime updates✦Increased online sales by 25% in the first 6 months✦Positive feedback from users✦Design first approach✦Blazing fast performance✦Highly scalable architecture✦Aesthetic UI✦Intuitive UX✦Highly secure✦Easy to use✦Easy to maintain✦Tested thoroughly✦Highly performant✦Realtime updates
We built a custom commerce platform with advanced features for a retail client to sell products online.
This is very good - imperfections don't matter. It will boost productivity. Sync from Figma to generate components. AI empowers everyone to create. Developers had exclusive creative power, but now anyone can create with AI. This is happening now, but history shows things revert to normal. An overview of the FL pipeline implemented with Flower for this example is shown in the diagram above.
It has four distinct stages:
At the beginning of a round, the server samples some clients and sends them the classification head (i.e. the part of the model being federated).
Each client, with a frozen pre-trained Whisper encoder, trains the classification head using its own data.
Once on-site training is completed, each client communicates the updated classification head back to the server.
The server aggregates the classification heads and obtains a new global classification head that will be communicated to clients in the next round.
