Federated learning, or collaborative learning, is a collaborative machine learning method that operates without changing original data. Unlike standard machine learning approaches that require centralising the training data into one machine or datacentre, federated learning trains algorithms across multiple decentralised edge devices or servers. This learning technique enables mobile phones to learn a shared prediction model while keeping the training data on the device itself and without having to store data in the cloud.

Today, we list down ten resources that will help you learn about federated learning.

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Federated learning using PyTorch: Udemy 

Created by ML enthusiast Mohamed Gharibi, this course on Udemy is targeted towards all federated learning enthusiasts. It takes the approach of looking at original papers’ techniques and algorithms and ultimately implementing federated learning techniques, including FedAvg, FedProx, FedDANE, and FedSGD.

Additionally, it provides an introduction to deep learning, neural networks, PySyft, and differential privacy. It teaches learners to build neural networks from scratch, implementing FedAvg using differential privacy and FedAvg on cloud, loading datasets in IID and non-IID.

After completing this course, students will be able to implement different federated learning techniques and build their own optimiser and techniques. This Udemy course is split into six sections across 32 lectures. The prerequisites include knowledge of the Python programming language. To know more about the content of the course, click here.

Advanced Deployment Scenarios with TensorFlow: Coursera

Offered by DeepLearning.AI, this course is delivered by Lead AI Advocate at Google, Laurence Moroney. Although this intermediate-level course focuses on TensorFlow, the instructor dives deep to explore federated learning and how one can retain deployed models with user data while maintaining data privacy.

The module consists of an introduction by Andrew Ng, followed by training on mobile devices, data at the edge, explaining how federated learning works, maintaining user privacy, masking, and finally. APIs and examples of federated learning. At the end of the federated learning module, the learner needs to take a quiz. The course approximately takes 13 hours to complete. To know more about the syllabus and enrollment options, click here.


Federated Learning – Synthesis…

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