摘要：In the evolving landscape of federated learning (FL), ensuring the active participation of local model owners (LMOs) while safeguarding data privacy and service security presents a formidable challenge. Our investigation focuses on two different information scenarios: the weakly incomplete information scenario and the strongly incomplete information scenario, which pose unique challenges to the integrity and efficiency of FL systems. In the weakly incomplete information scenario, LMOs have motivations to hide their true types. We use contract theory and exploit its self-revealing properties to ensure LMOs truthfully report their types. In the strongly incomplete information scenario, We present the Contract-based Deep Reinforcement Learning (CDRL) algorithm, which combines the strategic framework of contract theory with the adaptive capabilities of DRL. The CDRL algorithm is designed to perform real-time contract design in dynamic environments, enabling the system to respond effectively to FL participation and ensure continuous alignment of incentives with system security and learning objectives. Through extensive experimentation on a real-world dataset, our proposed mechanism has demonstrated superiority in motivating LMOs to actively participate in FL, thereby significantly improving system performance. ? 2024 IEEE.

ISSN号：无

卷、期、页：Proceedings of the IEEE International Conference on Web Services：535-544

发表日期：2024-07-07

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：Proceedings of the IEEE International Conference on Web Services, ICWS 2008

参与作者：陈莹

通讯作者：马博闻,冯子涵,高煜洲

第一作者：黄霁崴

论文类型：会议论文

论文概要：马博闻,冯子涵,高煜洲,陈莹,黄霁崴，Secure Service-Oriented Contract Based Incentive Mechanism Design in Federated Learning via Deep Reinforcement Learning，Proceedings of the IEEE International Conference on Web Services, ICWS 2008，2024，Proceedings of the IEEE International Conference on Web Services：535-544

论文题目：Secure Service-Oriented Contract Based Incentive Mechanism Design in Federated Learning via Deep Reinforcement Learning