Auckland AI & Entrepreneurship Event

Inspired by New Zealand’s growing focus on research commercialization, we are organizing an AI and Entrepreneurship event on the University of Auckland city campus. The event is intended for students, researchers, academics, and aspiring founders. We aim to answer a question: How does New Zealand turn AI research into companies that actually scale?

• 19 August 2026
• 2:00–5:30 pm
• University of Auckland Seminar Room 421W-501, City Campus (24 Symonds St, Building 421W, Room 501)

Program

Building Federated AI with Flower

Dr. Yan Gao · Flower Labs | University of Cambridge
LinkedIn · Flower Labs

• 5 June 2026
• 9:00–10:00 am
• Online

Abstract

Recent progress in large language models and foundation models has been driven by scaling compute, data, and model size. However, the path toward Artificial General Intelligence (AGI) may require access to data and compute that are distributed across devices, organizations, and real-world environments, where centralizing data is often impractical due to privacy, ownership, and regulatory constraints. In this lecture, I will explore why the first AGI may be federated. I will introduce the motivation and core ideas of federated learning, explain how it enables collaborative model training without moving raw data, and discuss why decentralized learning may become essential for future AI systems. I will also present Flower, an open-source framework for federated AI, and show how tools like Flower.ai can help build practical federated learning applications. Through this lecture, we will connect federated learning to the broader question of how AGI might be achieved.

Bio

Dr. Yan Gao is a Research Scientist at Flower Labs and Adjunct Researcher at the University of Cambridge, where his work is at the forefront of federated learning innovation. Prior to this role, he completed his PhD at the University of Cambridge in the Machine Learning Systems Lab. His research interests include machine learning, federated learning, self-supervised learning, and optimisation techniques. His work has focused on pioneering research in federated self-supervised learning, specifically targeting the challenge of working with unlabelled data across diverse domains such as audio, image, and video. This work has been recognised and published in several top-tier international conferences and journals, including NeurIPS, ICLR, MLSys, ICCV, ECCV, Interspeech, ICASSP, IMWUT, and JMLR.

The first AGI will be Federated

Prof. Nicholas D. Lane · University of Cambridge | Flower Labs
niclane.org · CaMLSys Lab · flower.ai

• 28 November 2025
• 1:00–3:00 pm
• 303-G16, 38 Princes St, Seminar Room (56), University of Auckland

Schedule

Abstract

Current scaling laws indicate that future advances in AI will hinge on access to massive amounts of compute and data. How will we obtain the computing power and data resources required to sustain the AI progress the world has grown accustomed to? I believe all roads lead to federated learning, and approaches of this kind. In the relatively near future, decentralized and federated techniques in machine learning will be how the strongest LLMs (and foundation models more generally) are trained; and in time, how aspirational capabilities like AGI will finally be achieved, in part, due to the adoption of federated methodologies. In this talk, I will describe why the future of AI will be federated, and describe early solutions developed by Flower Labs and CaMLSys that address the underlying technical challenges that the world will face as we shift from a centralized data-center mindset to decentralized alternatives.

Bio

Nic Lane (niclane.org) is a full Professor in the Department of Computer Science and Technology at the University of Cambridge and holds a Royal Academy of Engineering Chair in Decentralized AI. He is also a Fellow of St. John’s College. At Cambridge, Nic leads the Cambridge Machine Learning Systems lab (CaMLSys). The mission of CaMLSys is to invent the next generation of breakthrough ML-centric systems. Alongside his academic roles, Nic is the co-founder and Chief Scientific Officer of Flower Labs, a venture-backed AI company (YCW23) behind the Flower open-source federated learning framework. Flower Labs seeks to enable an AI future that is collaborative, open and decentralized. Nic has received multiple best paper awards, including ACM/IEEE IPSN 2017 and two from ACM UbiComp (2012 and 2015). In 2018 and 2019, he (and his co-authors) received the ACM SenSys Test-of-Time award and ACM SIGMOBILE Test-of-Time award for pioneering research, performed during his PhD thesis, that devised machine learning algorithms used today on devices like smartphones. Nic was the 2020 ACM SIGMOBILE Rockstar award winner for his contributions to “the understanding of how resource-constrained mobile devices can robustly understand, reason and react to complex user behaviors and environments through new paradigms in learning algorithms and system design.” In 2011, Nic received his Ph.D. from Dartmouth College. He also holds an M.Eng from Cornell University and a BSc.(Hons) from the University of Waikato.