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DTSTART:20250330T010000
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DTSTART;TZID=Europe/Rome:20250303T140000
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SUMMARY:Seminar: "A context-aware localized federated learning approach for cloudlet federation" by Sana Latif
DESCRIPTION:Abstract: This seminar presents a novel approach to federated learning (FL) within a cloudlet federation framework\, focusing on addressing the challenges of latency\, resource utilization\, and data privacy at the edge. Titled "A Context-aware Localized Federated Learning Approach for Cloudlet Federation\," the research leverages cloudlet-based edge computing to optimize deep learning model training for healthcare applications\, particularly COVID-19 detection. We will discuss using a local centralized broker and edge agents to manage network resources dynamically\, enabling efficient model aggregation and training. Key contributions include the implementation of a local Federated Averaging (FedAvg) algorithm\, which demonstrates improved accuracy\, reduced latency\, and faster convergence times compared to traditional FL methods. The results highlight the system's ability to handle non-IID data\, minimize network traffic\, and enhance resource utilization\, making it a viable solution for real-time\, healthcare applications.
URL:https://www.isislab.it/event/seminar-a-context-aware-localized-federated-learning-approach-for-cloudlet-federation-by-sana-latif/
CATEGORIES:Seminari
ATTACH;FMTTYPE=image/jpeg:https://www.isislab.it/wp-content/uploads/2025/02/Seminar-2025-03-03.jpg
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