{"id":22696029,"url":"https://github.com/snehilsanyal/self-learn-federated-learning","last_synced_at":"2026-02-08T16:31:12.200Z","repository":{"id":261366211,"uuid":"847809186","full_name":"snehilsanyal/self-learn-federated-learning","owner":"snehilsanyal","description":"🏫🙋🏻 [WIP] Self learn Federated Learning (FL). 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Self-curated. The difficulty will be moderate and expects prior knowledge about Python and Deep Learning concepts.\n\n## 👋🏻 Introductory Resources\n\n1. Start off with this funny and insightful comic on Federated Learning by [**Google AI**](https://ai.google/). The same site contains a list of learning resources and research done by Google on Federated Learning. \n    \n    **Link:**   [**Federated Learning Online Comic**](https://federated.withgoogle.com/)\n\n2. The first paper on Federated learning was by Google Inc in 2017. The authors presented a new learning paradigm where the data remains distributed in several mobile devices but the model is trained in a decentralized way. Instead of data moving to a centralized server, the model moves to distributed devices.\nQuoting from the Abstract (had to mention it lol):\n    \u003e **We advocate an alternative that leaves the training data distributed on\nthe mobile devices, and learns a shared model by\naggregating locally-computed updates. We term\nthis decentralized approach Federated Learning.**\n\n    **Blog:** [**Federated Learning: Collaborative Machine Learning without Centralized Training Data**](https://research.google/blog/federated-learning-collaborative-machine-learning-without-centralized-training-data/)\n\n    **Paper:** [**Communication-efficient learning of deep networks from decentralized data**](https://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf)\n    \n    Read the blog first, to get an overall picture of the work, then jump on to the paper for the nitty-gritty details.\n\n3. The next paper is again by Google Research. This time the paper deals with Keyword Prediction \n\n\n## ⚒💻 Hands-Dirty FL\n\n### 🗃💻 FL Libraries  \nIn the order of ease of usage (currently trying Flower, Syft and TF-Federated, will update the order):\n1. Flower by Flower Labs\n2. TensorFlow Federated by [**Google PARFAIT**](https://github.com/google-parfait)\n3. PySyft by [**OpenMined**](https://openmined.org/)\n4. FATE by FedAI Ecosystem\n5. CLARA by NVIDIA\n6. Substra by Owkin\n\n\n\n### 🤷🏻‍♂️💻 FL Tutorials\n\n\n\n\n## FL Usecases\n\n## 📰📜 FL Papers\n\n## 📚📔 FL Books \n\n## FL Courses \n\n1. [**DL.AI X Flower Labs, Short Course on Intro to Federated Learning**]\n2. [**DL.AI X Flower Labs, Short Course on Federated Fine-tuning of LLMs with Private Data**]\n\n## FL Blogs\n\n## FL Research Labs \n\n1. [**Federated GitHub by Google Research:**](https://github.com/google-research/federated)A collection of Google research projects related to Federated Learning and Federated Analytics.\n2. \n\n## FL Communities\n\n1. [**The Federated Learning Portal:**](https://federated-learning.org/) This portal keeps track of books, workshops, conferences, special tracks, and other events related to the field of FL. I came to know about many competitions in the domain of FL from this webpage.\n2. [**OpenMined Slack**](https://openmined.slack.com/signup#/domain-signup)\n3. [**Flower Labs Slack**](https://friendly-flower.slack.com/join/shared_invite/zt-2n8akh4dw-T9we9L6yXSp_z_ofodL4GQ#/shared-invite/email)\n4. [**FedML Discord**](https://discord.com/invite/9xkW8ae6RV)\n5. \n\n## 🐱‍💻🕹 FL Hackathons, Competitions and Challenges\n\n\n\n\n## 🏢🏛 FL Organizations \n\n1. OpenMined\n\n## FL Companies\n\n1. Flower Labs\n2. Owkin\n3. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnehilsanyal%2Fself-learn-federated-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsnehilsanyal%2Fself-learn-federated-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnehilsanyal%2Fself-learn-federated-learning/lists"}