Leveraging federated learning and edge computing for recommendation systems within cloud computing networks

Yuan Feng; Yaqian Qi; Hanzhe Li; Xiangxiang Wang; Jingxiao Tian

doi:10.1117/12.3034773

11 July 2024 Leveraging federated learning and edge computing for recommendation systems within cloud computing networks

Yuan Feng, Yaqian Qi, Hanzhe Li, Xiangxiang Wang, Jingxiao Tian

Author Affiliations +

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321012 (2024) https://doi.org/10.1117/12.3034773
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independently.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuan Feng, Yaqian Qi, Hanzhe Li, Xiangxiang Wang, and Jingxiao Tian "Leveraging federated learning and edge computing for recommendation systems within cloud computing networks", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321012 (11 July 2024); https://doi.org/10.1117/12.3034773

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KEYWORDS

Machine learning

Data modeling

Education and training

Instrument modeling

Data privacy

Systems modeling

Cloud computing

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