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Awesome-Federated-Machine-Learning: An Open Source Educational Resource for AI & Machine Learning Security | OpenSec Atlas

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AI & Machine Learning Security

Educational Resource

Awesome-Federated-Machine-Learning

A comprehensive curated repository aggregating the latest research, tools, tutorials, and resources on federated learning to support privacy-preserving collaborative AI model training.

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About This Tool

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

Primary Use Case

This repository serves as an educational and research resource for data scientists, AI researchers, and security professionals interested in federated learning. It provides access to cutting-edge papers, codebases, tutorials, and videos to facilitate understanding, development, and security assessment of federated learning systems.

Key Features

Curated collection of federated learning research papers from top ML conferences
Links to books, tutorials, videos, and blogs on federated learning
Categorized resources covering model aggregation, personalization, security, efficiency, and fairness
Open-source federated learning frameworks and enterprise-grade tools
Focus on federated learning security including AI model security scanners and risk assessment
Regular updates tracking latest advancements and workshops
Coverage of diverse application domains and machine learning paradigms
Organized references to top conferences and workshops related to federated learning

Insights & Recommendations

This repository is primarily an educational and reference resource rather than an installable tool or framework; users should follow links to individual projects or papers for implementation details. It is recommended to verify the currency of linked resources as federated learning is a rapidly evolving field.

Smart Usage Notes

Leverage the repository to train AI models that detect anomalous federated learning behaviors indicative of adversarial manipulation.Use curated security papers and tools to develop risk assessment frameworks tailored for federated learning deployments.Integrate federated learning security scanners from the repo into continuous monitoring pipelines for early threat detection.Employ tutorials and open-source frameworks to upskill blue teams on federated learning attack vectors and defenses.Utilize research on model aggregation and personalization to enhance robustness against poisoning and evasion attacks.