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PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.16.0//NONSGML v1.0//EN
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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20230101T000000
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DTSTART;TZID=Asia/Hong_Kong:20240503T140000
DTEND;TZID=Asia/Hong_Kong:20240503T150000
DTSTAMP:20260512T175216
CREATED:20240417T011530Z
LAST-MODIFIED:20250114T063925Z
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SUMMARY:RPG Seminar – Unified Hierarchical Federated Learning: Bridging Autonomous Driving and Construction Inspection
DESCRIPTION:Meeting ID: 957 7820 8166\nPassword: 631839 \nSpeaker:\nMr. Weibin KOU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAbstract:\nIn this seminar\, I will present the application of hierarchical federated learning (HFL) to address challenges in two distinct domains: autonomous driving and construction quality defect inspection. For autonomous driving\, we introduce an optimization-based framework\, Communication Resource Constrained Hierarchical Federated Learning (CRCHFL)\, which enhances HFL by incorporating optimization scheme to improve communication efficiency and model generalization under constrained communication resources. The effectiveness of this framework is validated through simulations\, showing significant improvements over traditional federated learning approaches. In the construction sector\, we propose a HFL framework tailored for privacy-preserving collaboration among robots performing quality defect inspections. This method utilizes a lightweight deep learning model suitable for resource-constrained robots\, focusing on image-based crack segmentation to ensure the safety and serviceability of infrastructures. Experimental results demonstrate that this federated approach outperforms the other baselines. Both implementations underline the versatility and efficiency of HFL in processing large datasets across distributed environments while adhering to privacy constraints\, offering substantial improvements in both operational efficiency and data security. \nBiography of the speaker:\nMr. Weibin KOU is currently working toward a Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His research interests include federated learning\, autonomous driving and robotic perception\, and Large Models (LMs). \nOrganizer:\nProf. Yik-Chung WU \nAll are welcome.
URL:https://ece.hku.hk/events/20240503-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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