Qiang Yang, Anbu Huang, Lixin Fan, Chee Seng Chan, Jian Han Lim, Kam Woh Ng, Ding Sheng Ong, Bowen Li. Federated Learning with Privacy-preserving and Model IP-right-protection. Machine Intelligence Research, vol. 20, no. 1, pp.19-37, 2023. https://doi.org/10.1007/s11633-022-1343-2
Citation: Qiang Yang, Anbu Huang, Lixin Fan, Chee Seng Chan, Jian Han Lim, Kam Woh Ng, Ding Sheng Ong, Bowen Li. Federated Learning with Privacy-preserving and Model IP-right-protection. Machine Intelligence Research, vol. 20, no. 1, pp.19-37, 2023. https://doi.org/10.1007/s11633-022-1343-2

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

doi: 10.1007/s11633-022-1343-2
More Information
  • Author Bio:

    Qiang Yang is a Fellow of Canadian Academy of Engineering (CAE) and Royal Society of Canada (RSC), Chief Artificial Intelligence Officer of WeBank and Chair Professor of CSE Department, Hong Kong University of Science and Technology, China. He is the Conference Chair of AAAI-21, President of Hong Kong Society of Artificial Intelligence and Robotics (HKSAIR), the President of Investment Technology League (ITL) and Open Islands Privacy-Computing Open-source Community, and former President of IJCAI (2017−2019). He is a fellow of AAAI, ACM, IEEE and AAAS. He is the founding EiC of two journals: IEEE Transactions on Big Data and ACM Transactions on Intelligent Systems and Technology. His latest books are Transfer Learning, Federated Learning, Privacy-preserving Computing and Practicing Federated Learning. His research interests include transfer learning and federated learning. E-mail: qyang@cse.ust.hk (Corresponding author)ORCID iD: 0000-0001-5059-8360

    Anbu Huang is currently a senior research scientist at WeBank, his research papers have been published in leading journals and conferences, such as AAAI and ACM TIST. He served as a peer reviewer in ACM TIST, IEEE TMI, IJCAI, and other top artificial intelligence journals and conferences. Previously, He was a technical team leader at Tencent (2014−2018), and a senior engineer at MicroStrategy (2012−2014). His latest books are Practicing Federated Learning (2021) and Dive into Deep Learning (2017).His research interests include deep learning, machine learning, and federated learning. E-mail: stevenhuang@webank.com ORCID iD: 0000-0003-3444-7348

    Lixin Fan is the Chief Scientist of Artificial Intelligence at WeBank, China. He is the author of more than 70 international journals and conference articles. He has worked at Nokia Research Center and Xerox Research Center Europe. He has participated in NIPS/NeurIPS, ICML, CVPR, ICCV, ECCV, IJCAI and other top artificial intelligence conferences for a long time, served as area chair of ICPR, and organized workshops in various technical fields. He is also the inventor of more than one hundred patents filed in USA , Europe and China, and the chairman of the IEEE P2894 Explainable Artificial Intelligence (XAI) Standard Working Group.His research interests include machine learning and deep learning, computer vision and pattern recognition, image and video processing, 3D big data processing, data visualization and rendering, augmented and virtual reality, mobile computing and ubiquitous computing, and intelligent man-machine interface. E-mail: lixinfan@webank.com

    Chee Seng Chan is currently a full Professor with Faculty of Computer Science and Information Technology, University of Malaya, Malaysia. Dr. Chan was the Founding Chair for the IEEE Computational Intelligence Society Malaysia Chapter, the Organising Chair for the Asian Conference on Pattern Recognition (2015), the General Chair for the IEEE Workshop on Multimedia Signal Processing (2019), and IEEE Visual Communications and Image Processing (2013). He is a Chartered Engineer registered under the Engineering Council, UK. He was the recipient of several notable awards, such as Young Scientist Network-Academy of Sciences Malaysia in 2015 and the Hitachi Research Fellowship in 2013.His research interests include computer vision and machine learning with focus on scene understanding. He is also interested in the interplay between language and vision: generating sentential descriptions about complex scenes.E-mail: cs.chan@um.edu.cn

    Jian Han Lim is currently a Ph. D. degree candidate in artificial intelligence with Universiti Malaya, Malaysia. His research interests include computer vision and deep learning with a focus on image captioning.E-mail: jianhanl98@gmail.com

    Kam Woh Ng received the B. Sc. degree from Faculty of Computer Science and Information Technology, University of Malaya, Malaysia, in 2019. He is currently a Ph. D. degree candidate at University of Surrey, UK under the supervision of Prof. Tao Xiang and Prof. Yi-Zhe Song. Prior to joining the University of Surrey, he was an AI researcher from WeBank, China and a lab member of Center of Image and Signal Processing (CISIP) in University of Malaya, Malaysia. His research interests include deep learning, computer vision, representation learning and their applications.E-mail: kamwoh.ng@surrey.ac.uk ORCID iD: 0000-0002-9309-563X

    Ding Sheng Ong received the B. Sc. degree from Faculty of Computer Science and Information Technology, University of Malaya, Malaysia, in 2020. He currently a Ph. D. degree candidate at is Aberystwyth University, UK. Prior to joining the Aberystwyth University, he was a lab member of Center of Image and Signal Processing (CISiP) in Universiti Malaya, Malaysia. His research interests include deep learning and computer vision.E-mail: sheng970303@gmail.com

    Bowen Li received the B. Sc. degree in automation from Xi′an Jiaotong University, China in 2019. He is currently a Ph. D. degree candidate at Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. He worked as a research intern at WeBank AI Group, China in 2021. His research interests include federated learning, data privacy, and machine learning security.E-mail: li-bowen@sjtu.edu.cn ORCID iD: 0000-0003-1602-3541

  • Received Date: 2020-03-02
  • Accepted Date: 2020-06-02
  • 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.

     

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