https://github.com/snehilsanyal/self-learn-federated-learning
🏫🙋🏻 [WIP] Self learn Federated Learning (FL). This repository is a collection of resources, notebooks, blogs, and tutorials for beginners.
https://github.com/snehilsanyal/self-learn-federated-learning
federated-learning fedml self-learning
Last synced: 4 months ago
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🏫🙋🏻 [WIP] Self learn Federated Learning (FL). This repository is a collection of resources, notebooks, blogs, and tutorials for beginners.
- Host: GitHub
- URL: https://github.com/snehilsanyal/self-learn-federated-learning
- Owner: snehilsanyal
- License: mit
- Created: 2024-08-26T15:31:46.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-20T13:45:05.000Z (over 1 year ago)
- Last Synced: 2025-07-28T09:37:17.775Z (11 months ago)
- Topics: federated-learning, fedml, self-learning
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🏫🙋🏻 Self Learn Federated Learning
## ⚒ 💻 Work in Progress
This repository contains a roadmap for beginner's to start their journey on Federated Learning. Self-curated. The difficulty will be moderate and expects prior knowledge about Python and Deep Learning concepts.
## 👋🏻 Introductory Resources
1. 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.
**Link:** [**Federated Learning Online Comic**](https://federated.withgoogle.com/)
2. 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.
Quoting from the Abstract (had to mention it lol):
> **We advocate an alternative that leaves the training data distributed on
the mobile devices, and learns a shared model by
aggregating locally-computed updates. We term
this decentralized approach Federated Learning.**
**Blog:** [**Federated Learning: Collaborative Machine Learning without Centralized Training Data**](https://research.google/blog/federated-learning-collaborative-machine-learning-without-centralized-training-data/)
**Paper:** [**Communication-efficient learning of deep networks from decentralized data**](https://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf)
Read the blog first, to get an overall picture of the work, then jump on to the paper for the nitty-gritty details.
3. The next paper is again by Google Research. This time the paper deals with Keyword Prediction
## ⚒💻 Hands-Dirty FL
### 🗃💻 FL Libraries
In the order of ease of usage (currently trying Flower, Syft and TF-Federated, will update the order):
1. Flower by Flower Labs
2. TensorFlow Federated by [**Google PARFAIT**](https://github.com/google-parfait)
3. PySyft by [**OpenMined**](https://openmined.org/)
4. FATE by FedAI Ecosystem
5. CLARA by NVIDIA
6. Substra by Owkin
### 🤷🏻♂️💻 FL Tutorials
## FL Usecases
## 📰📜 FL Papers
## 📚📔 FL Books
## FL Courses
1. [**DL.AI X Flower Labs, Short Course on Intro to Federated Learning**]
2. [**DL.AI X Flower Labs, Short Course on Federated Fine-tuning of LLMs with Private Data**]
## FL Blogs
## FL Research Labs
1. [**Federated GitHub by Google Research:**](https://github.com/google-research/federated)A collection of Google research projects related to Federated Learning and Federated Analytics.
2.
## FL Communities
1. [**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.
2. [**OpenMined Slack**](https://openmined.slack.com/signup#/domain-signup)
3. [**Flower Labs Slack**](https://friendly-flower.slack.com/join/shared_invite/zt-2n8akh4dw-T9we9L6yXSp_z_ofodL4GQ#/shared-invite/email)
4. [**FedML Discord**](https://discord.com/invite/9xkW8ae6RV)
5.
## 🐱💻🕹 FL Hackathons, Competitions and Challenges
## 🏢🏛 FL Organizations
1. OpenMined
## FL Companies
1. Flower Labs
2. Owkin
3.