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21 April 2025

  • 16:1316:13, 21 April 2025 About (hist | edit) [1,668 bytes] 141.215.80.152 (talk) (Created page with "CIS 489/589 Edge Computing at University of Michigan -- Dearborn Winter 2025, 3 Credit Hours Meeting Times: Thursday 6:00 PM to 8:30 PM Location: IAVS Room 2025 This course introduces state-of-the-art edge computing technologies and their applications in data-intensive distributed systems like smart homes, Internet of Things, and connected vehicles. Topics include edge computing applications and platforms, edge-based sensor data collection and processing, computatio...")

8 April 2025

  • 17:3817:38, 8 April 2025 Conclusion (hist | edit) [3,414 bytes] Zhesong (talk | contribs) (Created page with "Conclusion and Future Outlook Edge computing is rapidly transforming the way we think about data, computation, and intelligence in the digital age. By shifting computing resources closer to where data is generated—whether in sensors, mobile devices, vehicles, or smart infrastructure—edge computing offers solutions to the growing demands of low-latency processing, privacy preservation, and bandwidth efficiency. As data volumes surge and real-time decision-making becom...")

6 April 2025

5 April 2025

  • 23:3823:38, 5 April 2025 Applications of Edge Computing (hist | edit) [22,212 bytes] Anasrin (talk | contribs) (Created page with "==8.1 Introduction to UIoT and AUVs== The Underwater Internet of Things (UIoT) is an extension of the Internet of Things (IoT) to the aquatic environments, enabling seamless data collection, communication, and automation beneath the ocean’s surface. It represents a transformative shift in marine technology. UIoT systems basically consist of interconnected smart devices—such as sensors, autonomous vehicles, and underwater drones, that monitor and interact with underw...")

4 April 2025

3 April 2025

1 April 2025

29 March 2025

  • 13:5413:54, 29 March 2025 Federated Learning (hist | edit) [47,038 bytes] Idvsrevanth (talk | contribs) (Created page with "== Federated Learning in Edge Computing == === 1. Overview and Fundamentals === '''Federated Learning (FL)''' is a decentralized machine learning paradigm where edge devices (clients) collaboratively train a global model under the orchestration of a central or distributed aggregator, while retaining all local data on-device. This approach aligns closely with edge computing goals of privacy, efficiency, and low-latency intelligence. Key benefits include: * Preserving u...")

1 March 2025

27 February 2025

13 February 2025