Prof. YANG Qiang co-authored the paper “Federated Machine Learning: Concept and Applications”, which received the Frontiers of Science Award at ICBS 2025.

The 2025 International Congress of Basic Science (ICBS) officially opened in Beijing on July 13 and will run through July 25. Since its inception in 2023 under the leadership of Academician Shing-Tung Yau, ICBS has become a premier international platform in the field of basic science. The Congress focuses on three major areas—mathematics, physics, and information science and engineering—and gathers global elites to drive disciplinary breakthroughs. This year’s event features an esteemed lineup, including Nobel, Turing, and Fields Medal laureates such as Samuel C. C. Ting and Steven Chu. Two major awards—the Lifetime Achievement Award in Basic Science and the Frontier Science Award—were presented during the Congress.

One of the highlights of this year’s event was the recognition of the paper "Federated Machine Learning: Concept and Applications," co-authored by Professor Qiang Yang (Chair Professor and Director of the Academy of Artificial Intelligence, The Hong Kong Polytechnic University), Associate Professor Yang Liu (Department of Computing and Department of Data Science and Artificial Intelligence), Dr. Tianjian Chen, and Professor Yongxin Tong. The paper was honored with the 2025 Frontier Science Award.

Professor Yang Liu accepted the award on behalf of all the authors and delivered a speech on behalf of the awardees in the field of information science and engineering. The Congress recognized the paper for “addressing the framework of federated machine learning for privacy preservation, introducing a decentralized training paradigm without data sharing, and pioneering horizontal and vertical federated learning methods. This work has tackled critical issues such as data heterogeneity and security, with wide applications in healthcare, finance, and the Internet of Things, paving new paths for integrating AI with the real economy.”

This prestigious award highlights the strong capabilities of the research team and reflects the deep academic foundation of the PolyU Academy of Artificial Intelligence (PAAI) and its Research Institute for Federated Learning (RIFL).

Federated learning is an advanced distributed AI technology that enables multiple devices to collaboratively train models without sharing raw data. By protecting data privacy, it facilitates secure and efficient cross-domain intelligent collaboration. It has become a core paradigm of privacy-preserving artificial intelligence.

Established in 2025, the PolyU Academy of Artificial Intelligence (PAAI) is co-led by Professor Qiang Yang and Professor Hongxia Yang. It focuses on developing industry-specific large language models and exploring decentralized generative AI, contributing to Hong Kong's leadership in innovation.

Under PAAI, the Research Institute for Federated Learning (RIFL) is one of the world’s first research institutes dedicated to federated learning. Co-directed by Professor Yang Liu and Professor Qiang Yang, RIFL aims to advance fundamental research and real-world applications in this cutting-edge area.

Moving forward, RIFL will continue to work closely with PAAI under the leadership of Professor Yang Liu and Professor Qiang Yang, striving to advance the global development of federated learning to new heights.