A novel differentially private advising framework in cloud server environment. (20th July 2020)

• Record Type: Journal Article
• Authors: Shen, Sheng; Zhu, Tianqing; Ye, Dayong; Wang, Minghao; Zuo, Xuhan; Zhou, Andi
• Is Part Of: Concurrency and computation. Volume 34:Number 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-20
• Subjects: advising -- cloud servers -- differential privacy -- reinforcement learning; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5932
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.