首页|Technical University of Denmark (DTU) Researcher Furthers Understanding of Machi ne Learning (Federated In-Network Machine Learning for Privacy-Preserving IoT Tr affic Analysis)
Technical University of Denmark (DTU) Researcher Furthers Understanding of Machi ne Learning (Federated In-Network Machine Learning for Privacy-Preserving IoT Tr affic Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting out of Kongens Lyngby, Denmark, by NewsRx editors, research stated, “The expanding use of IoT has driven machine learning (ML) based traffic analysis.” The news reporters obtained a quote from the research from Technical University of Denmark (DTU): “5G networks’ standards, requiring low-latency communications for time-critical services, pose new challenges to traffic analysis. They necess itate fast analysis and response, preventing service disruption or security impa ct on network infrastructure. Distributed intelligence on IoT edge has been stud ied to analyze traffic, but introduces delays and raises privacy concerns. Feder ated learning can address privacy concerns, but does not meet latency requiremen ts. In this paper, we propose FLIP4: an efficient federated learning-based frame work for in-network traffic analysis. Our solution introduces a lightweight fede rated tree-based model, offloaded and running within network devices.”
Technical University of Denmark (DTU)K ongens LyngbyDenmarkEuropeCybersecurityCyborgsEmerging TechnologiesM achine Learning
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