The Vancouver Vision & Learning Workshop @ ICML 2025, jointly organized by Simon Fraser University (SFU), the University of British Columbia (UBC), and the Vector Institute, was successfully held on July 14, 2025. As a key affiliated event of the prestigious International Conference on Machine Learning (ICML 2025), the workshop brought together leading scholars and industry experts to explore cutting-edge advancements in computer vision and machine learning.
Among the highlights of the event was a keynote presentation by Professor Qiang Yang, Director of the Hong Kong Polytechnic University’s PolyU Academy for Artificial Intelligence (PAAI), titled “Federated Learning meets Large Language Models.” Professor Yang's talk attracted significant attention for its in-depth exploration of federated learning—an emerging paradigm in distributed AI that enables collaborative model training across multiple devices or institutions without sharing raw data. This approach plays a pivotal role in building privacy-preserving and efficient cross-domain AI systems.
Professor Yang further discussed several promising directions and applications of federated learning, including:
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Federated Foundation Models that integrate pre-trained large language models with domain-specific models;
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Agentic Federated Learning, which leverages large language models to develop intelligent edge agents;
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Industry Collaborations, especially in the financial sector, involving both domestic and international institutions to promote real-world applications;
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Scientific Research, enhancing cross-institutional collaboration through privacy-preserving AI techniques;
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And the development of open-source tools to support technology implementation and ecosystem growth.
In addition to Professor Yang, the workshop featured insightful presentations by leading researchers including Kelsey Allen (UBC, Vector Institute), Jamie Shotton (Wayve), Masashi Sugiyama (RIKEN AIP, University of Tokyo), Alane Suhr (UC Berkeley), and Arash Vahdat (NVIDIA). Their talks covered a wide array of topics spanning vision, learning, and language, highlighting groundbreaking intersections between artificial intelligence, the physical world, and cognitive science.
The workshop not only served as a high-level platform for academic exchange but also underscored the importance of cross-institutional collaboration in advancing the frontiers of AI research. These explorations continue to inject new momentum into the field and demonstrate the vast potential of interdisciplinary integration.
