Resources for Federated Learning
To deepen your understanding of Federated Learning, here is a curated list of resources. These include seminal research papers, open-source frameworks, insightful tutorials, comprehensive courses, and active communities. Whether you are a researcher, developer, or enthusiast, these resources will help you navigate the FL landscape.
📄Key Research Papers
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Communication-Efficient Learning of Deep Networks from Decentralized Data (McMahan et al., 2017)
The foundational paper that introduced the Federated Averaging (FedAvg) algorithm.
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Advances and Open Problems in Federated Learning (Kairouz et al., 2019)
A comprehensive survey covering the progress, challenges, and open questions in FL.
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Towards Federated Learning at Scale: System Design (Bonawitz et al., 2019)
Discusses the system design principles for building large-scale FL systems.
🛠️Open Source Frameworks
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TensorFlow Federated (TFF)
An open-source framework by Google for machine learning and other computations on decentralized data.
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PySyft (OpenMined)
A Python library for secure and private Deep Learning, allowing for federated learning with differential privacy and multi-party computation.
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Flower
A friendly federated learning framework that is adaptable to various ML pipelines and client environments.
🎓Tutorials, Courses & Books
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Federated Learning Tutorials by TFF
Official tutorials to get started with TensorFlow Federated.
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Private AI Series by OpenMined on Udacity
Courses covering privacy-preserving AI techniques, including Federated Learning.
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Federated Learning: Synthesis Lectures on Artifical Intelligence and Machine Learning (Yang et al., 2020)
A book offering a structured overview of FL principles and algorithms.
💬Communities & Conferences
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OpenMined Community
A large and active community focused on privacy-preserving AI technologies.
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FL @ ICML, NeurIPS, ICLR
Many top AI/ML conferences now have dedicated workshops or tracks for Federated Learning research.
💡Continuous Learning: The field of FL is dynamic. Regularly check for new publications, framework updates, and community discussions to stay current. Consider contributing to open-source projects to gain hands-on experience!
Exploring these resources will provide a robust understanding of both the theoretical underpinnings and practical implementations of Federated Learning. For a broader view on how AI is changing various fields, you might find The Future of Work: AI-Powered Collaboration Tools an interesting read.