首页|Reports Outline Machine Learning Findings from University of Rovira and Virgili (Fair Detection of Poisoning Attacks In Federated Learning On Non-i.i.d. Data)
Reports Outline Machine Learning Findings from University of Rovira and Virgili (Fair Detection of Poisoning Attacks In Federated Learning On Non-i.i.d. Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Catalonia, Spain, by New sRx journalists, research stated, “Reconciling machine learning with individual privacy is one of the main motivations behind federated learning (FL), a decentr alized machine learning technique that aggregates partial models trained by clie nts on their own private data to obtain a global deep learning model. Even if FL provides stronger privacy guarantees to the participating clients than centrali zed learning collecting the clients’ data in a central server, FL is vulnerable to some attacks whereby malicious clients submit bad updates in order to prevent the model from converging or, more subtly, to introduce artificial bias in the classification (poisoning).”
CataloniaSpainEuropeCyborgsEmerg ing TechnologiesHealth and MedicineMachine LearningPoisoningUniversity o f Rovira and Virgili
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