Ryanditko/federated-learning-at-cybersecurity
Este projeto de iniciação científica tem como objetivo investigar e desenvolver soluções baseadas em Aprendizado Federado, um paradigma de inteligência artificial descentralizada que permite o treinamento colaborativo de modelos de machine learning sem a necessidade de centralizar dados sensíveis.
When multiple organizations collaborate to build a machine learning model using their private data, this project helps detect and neutralize malicious attempts to corrupt the shared model. It takes model updates from contributing parties and identifies harmful contributions, outputting a more secure, robust global model. This is for cybersecurity professionals, data scientists, or IT managers responsible for securing AI systems that learn from distributed, sensitive data.
Use this if you are building or managing a federated learning system and need to protect it from 'poisoning attacks' that could degrade its performance or introduce vulnerabilities.
Not ideal if your machine learning models are trained on centralized, trusted data sources, as the core problem this solves (distributed data privacy and security) would not apply.
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Last pushed
Mar 12, 2026
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