The 2026 Conference on Machine Learning and Data Security (MLDS 2026) Concludes in Guangzhou

On June 12, 2026, the Conference on Machine Learning and Data Security (MLDS 2026) was held in Guangzhou at the Multifunctional Conference Room, 4th Floor, Huanghuagang Science and Technology Park, National High-Tech Zone. The event brought together researchers and practitioners from around the world working in machine learning and data security.

Group Photo

The program opened with a welcome address byMs. Shaolan Huang, Director of the Yuexiu District Science, Technology, Industry and Information Technology Bureau, Guangzhou, followed by a group photo.The academic program featured four keynote speeches:

Ms. Shaolan Huang, Yuexiu District Science, Technology, Industry and Information Technology Bureau, Guangzhou

Welcome Remark

Prof. Zhuliang Yu, South China University of Technology, China

Speech Title: Research on the Application of Machine Learning in Brain Source Imaging

Prof. Wenjian Luo, Harbin Institute of Technology, Shenzhen, China

Speech Title: Reverse Engineering Principles and Methods for Deep Learning Model Training Data

Prof. Hong-Liang Dai, Guangzhou University, China

Speech Title: Secure and Trustworthy AI: Adaptive Multiple Kernel Learning and Generative Industrial Diagnosis

Assoc. Prof. Feng Yin, The Chinese University of Hong Kong, Shenzhen, China

Speech Title: Automated Kernel Design for Bayesian Learning and Optimization

In addition to the keynotes, one oral presentation was given. Runtong Xu (Beijing University of Technology) presented a verifiable federated learning framework using zero-knowledge proofs for differential privacy noise.

Runtong Xu, Beijing University of Technology, China

Title: ZKDP-FL: Verifiable Federated Learning with Zero-Knowledge Proofs of Differential Privacy Noise

A special session on industry-academia collaboration was also held. QLYBOT (Guangzhou) Technology Co., Ltd. showcased industrial robot technologies, and TIANJIANGTONG (Guangdong) Digital Technology Co., Ltd. shared a technology transfer case study. These sessions fostered interaction between academia and industry.

We look forward to future editions of the conference.