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in this course, you learned about federated learning and the important role it plays in unlocking large amounts of currently unused distributed training data using the Flower of federated learning framework. You built different versions of federated training pipelines. You learned how to tune different aspects of federated systems, how to think about data privacy, and how to calculate and measure the bandwidth usage of federated systems using Flower. So what's next? This is course one of a two course series. The next course will introduce federated LLM fine tuning on private data. I also want to encourage you to start your own explorations. We'd love to hear from you on Flower.ai or at flw labs on x.com. Join the Flower community slack. There are thousands of like minded AI researchers and developers exchanging ideas. Thank you to the incredible Flower community. It's a real inspiration to see so many projects pushing the boundaries. I'm looking forward to seeing what you have built on your own.
course detail
Next Lesson
Intro to Federated Learning
  • Introduction
    Video
    ・
    4 mins
  • Why Federated Learning
    Video with Code Example
    ・
    18 mins
  • Federated Training Process
    Video with Code Example
    ・
    15 mins
  • Tuning
    Video with Code Example
    ・
    10 mins
  • Data Privacy
    Video with Code Example
    ・
    9 mins
  • Bandwidth
    Video with Code Example
    ・
    9 mins
  • Conclusion
    Video
    ・
    1 mins
  • Course Feedback
  • Community
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