Federated Learning with Privacy-preserving and Model IP-right-protection

Qiang Yang*, Anbu Huang, Lixin Fan, Chee Seng Chan, Jian Han Lim, Kam Woh Ng, Ding Sheng Ong, Bowen Li

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygl Adolyguadolygiad gan gymheiriaid

10 Dyfyniadau (Scopus)
113 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

In the past decades, artificial intelligence (AI) has achieved unprecedented success, where statistical models become the central entity in AI. However, the centralized training and inference paradigm for building and using these models is facing more and more privacy and legal challenges. To bridge the gap between data privacy and the need for data fusion, an emerging AI paradigm federated learning (FL) has emerged as an approach for solving data silos and data privacy problems. Based on secure distributed AI, federated learning emphasizes data security throughout the lifecycle, which includes the following steps: data preprocessing, training, evaluation, and deployments. FL keeps data security by using methods, such as secure multi-party computation (MPC), differential privacy, and hardware solutions, to build and use distributed multiple-party machine-learning systems and statistical models over different data sources. Besides data privacy concerns, we argue that the concept of “model” matters, when developing and deploying federated models, they are easy to expose to various kinds of risks including plagiarism, illegal copy, and misuse. To address these issues, we introduce FedIPR, a novel ownership verification scheme, by embedding watermarks into FL models to verify the ownership of FL models and protect model intellectual property rights (IPR or IP-right for short). While security is at the core of FL, there are still many articles referred to distributed machine learning with no security guarantee as “federated learning”, which are not satisfied with the FL definition supposed to be. To this end, in this paper, we reiterate the concept of federated learning and propose secure federated learning (SFL), where the ultimate goal is to build trustworthy and safe AI with strong privacy-preserving and IP-right-preserving. We provide a comprehensive overview of existing works, including threats, attacks, and defenses in each phase of SFL from the lifecycle perspective.

Iaith wreiddiolSaesneg
Tudalennau (o-i)19-37
Nifer y tudalennau19
CyfnodolynMachine Intelligence Research
Cyfrol20
Rhif cyhoeddi1
Dyddiad ar-lein cynnar10 Ion 2023
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Chwef 2023

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