Zhen Li

Beijing Institute of Technology PhD Student, Beijing Institute of Technology (2022)

Zhen Li is currently currently pursuing the PhD degree with the School of Computer Science and Technology at Beijing Institute of Technology, located in Beijing, China. Under the expert guidance of Professor Zijian Zhang, Zhen's academic pursuits are centered on Privacy Computing and blockchain technology. His work is focused on advancing secure and innovative solutions that harness the power of these technologies to protect data privacy and enhance transactional integrity.

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Education
  • Beijing Institute of Technology

    Beijing Institute of Technology

    PhD Student. in Computer Technology Sep. 2022 - Now

  • Beiijing Information Science and Technology University

    Beiijing Information Science and Technology University

    M.S. in Computer Applied Technology Sep. 2018 - Jun. 2021

  • Beiijing Information Science and Technology University

    Beiijing Information Science and Technology University

    B.S. in Computer Science and Techology Sep. 2014 - Jun. 2018

Selected Publications (view all )
A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

Yuming Tang#, Yitian Zhang#, Tao Niu, Zhen Li*, Zijian Zhang, Huaping Chen, Long Zhang (# equal contribution, * corresponding author)

Computer Modeling in Engineering and Sciences 2024

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which is a bottleneck in the development and industrialization of federated learning. Blockchain technology, characterized by its decentralized ledger system, offers a compelling solution to these issues. It inherently prevents single-point failures and, through its incentive mechanisms, motivates participants to contribute computing power. Thus, blockchain-based FL (BCFL) emerges as a natural progression to address FL’s challenges. This study begins with concise introductions to federated learning and blockchain technologies, followed by a formal analysis of the specific problems that FL encounters. It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL. In addition, this research examines the use of BCFL in various fields, such as the Internet of Things and the Internet of Vehicles. Finally, it assesses the effectiveness of these solutions.

[Paper]

A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis
A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

Yuming Tang#, Yitian Zhang#, Tao Niu, Zhen Li*, Zijian Zhang, Huaping Chen, Long Zhang (# equal contribution, * corresponding author)

Computer Modeling in Engineering and Sciences 2024

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which is a bottleneck in the development and industrialization of federated learning. Blockchain technology, characterized by its decentralized ledger system, offers a compelling solution to these issues. It inherently prevents single-point failures and, through its incentive mechanisms, motivates participants to contribute computing power. Thus, blockchain-based FL (BCFL) emerges as a natural progression to address FL’s challenges. This study begins with concise introductions to federated learning and blockchain technologies, followed by a formal analysis of the specific problems that FL encounters. It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL. In addition, this research examines the use of BCFL in various fields, such as the Internet of Things and the Internet of Vehicles. Finally, it assesses the effectiveness of these solutions.

[Paper]

Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness

Yitian Zhang#, Yuming Tang#, Zijian Zhang, Meng Li, Zhen Li*, Salabat Khan, Huaping Chen, Guoqiang Cheng (# equal contribution, * corresponding author)

Mathematics 2023

Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.

[Paper]

Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness
Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness

Yitian Zhang#, Yuming Tang#, Zijian Zhang, Meng Li, Zhen Li*, Salabat Khan, Huaping Chen, Guoqiang Cheng (# equal contribution, * corresponding author)

Mathematics 2023

Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.

[Paper]

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