首页|University of Quebec Rimouski Researcher Provides Details of New Studies and Fin dings in the Area of Artificial Intelligence (Securing Federated Learning: Appro aches, Mechanisms and Opportunities)
University of Quebec Rimouski Researcher Provides Details of New Studies and Fin dings in the Area of Artificial Intelligence (Securing Federated Learning: Appro aches, Mechanisms and Opportunities)
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Fresh data on artificial intelligence are presented in a new report. According to news reporting from Rimouski, Canada , by NewsRx journalists, research stated, "With the ability to analyze data, art ificial intelligence technology and its offshoots have made difficult tasks easi er." Our news correspondents obtained a quote from the research from University of Qu ebec Rimouski: "The tools of these technologies are now used in almost every asp ect of life. For example, Machine Learning (ML), an offshoot of artificial intel ligence, has become the focus of interest for researchers in industry, education , healthcare and other disciplines and has proven to be as efficient as, and in some cases better than, experts in answering various problems. However, the obst acles to ML's progress are still being explored, and Federated Learning (FL) has been presented as a solution to the problems of privacy and confidentiality. In the FL approach, users do not disclose their data throughout the learning proce ss, which improves privacy and security. In this article, we look at the securit y and privacy concepts of FL and the threats and attacks it faces. We also addre ss the security measures used in FL aggregation procedures."
University of Quebec RimouskiRimouskiCanadaNorth and Central AmericaArtificial IntelligenceEmerging Technologi esMachine Learning
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