Скачать или смотреть Launching Your First Simulation - 2025 Tutorial: Federated AI Simulations with Flower

"Launching Your First Simulation" - Part one of our new 2025 tutorial series "Federated AI Simulations with Flower".

🌸 This comprehensive series takes you through everything you need to know to simulate Federated Learning using Flower. From launching your first simulation to scaling up and designing custom strategies, this 8-video series will guide you step-by-step to build and optimize your own federated AI pipeline. Whether you're new to Flower or looking to deepen your understanding, this series has something for everyone!

📝 You can find the code for this tutorial series here:
https://github.com/adap/flower/tree/m...

⭐️ We hope you enjoy this content! If you do, consider giving us a star on GitHub: https://github.com/adap/flower

🌼 Join the Flower community on Slack: https://flower.ai/join-slack/

💬 Join our Flower Discuss forum: https://discuss.flower.ai

🤖 What is Federated Learning? Federated Learning (FL) is the process of collaboratively training machine learning models without having each participant first send their dataset to a central server. Through multiple iterations of local training (by clients) followed by aggregation (on a central server and typically involving some form of averaging), the underlying learning algorithm or framework derives an updated global model. Under this formulation, FL is poised to be the preferred privacy-preserving learning approach to take the training where the raw data is, whether it is on smart or IoT devices or in institutions in the healthcare sector training, for instance, artificial intelligent agents to aid with different diagnoses. FL borrows from other domains in machine learning studying methods for on-device optimization, differentiable privacy, and continual learning.

🎓 Are you looking for a federated learning tutorial? This series offers a comprehensive step-by-step federated learning guide to show you how to set up a federated learning framework using Flower. If you’re new to FL, why not start with our beginner-friendly tutorials here: https://flower.ai/docs/framework/tuto...

🤗 This series is aimed at an audience that is a bit familiar with the key concepts in Federated Learning. If you are starting from zero… that’s fine too!! Be sure to subscribe to our channel because we plan on releasing much more beginner-friendly content in the future!

😎 If you are a pro already, then for sure, the last three videos of this series will be relevant to you. You’ll see what a minimal Federated Learning pipeline using Flower looks like, then you’ll learn how to make the code base much more versatile by using Hydra configs effectively. More advanced usage of Flower will be released very soon.

Your feedback is very important to us, so please tell us in the comments below what kind of video you would like to see next! And if you have some questions, feel free to ask either here or in our Slack workspace!

More code examples can be found in the Flower repository: https://github.com/adap/flower/tree/m...