FL@FM-ICME'24

International Workshop on Federated Learning and Foundation Models for Multi-Media (FL@FM-ICME'24)

Final Submission Deadline: ~~March 31, 2024 (23:59:59 AoE)~~
Notification Due: ~~April 15, 2024 (23:59:59 AoE)~~
Workshop Date: Monday, July 15, 2024
Venue: Room Peninsula 1F, Marriott Convention & Exhibition Centre, Niagara Falls, ON, Canada

Workshop Program (Monday, July 15, 2024)


Time	Activity

08:30 – 09:00	Keynote 1: Federated Learning in Healthcare: Opportunities and Barriers, by Ross Mitchell
09:00 – 09:30	Keynote 2: Building Multi-Foundation Agent Systems through Trustworthy Auction-based Federated Learning, by Han Yu
09:30 – 10:00	Keynote 3: Easy and Scalable Federated Learning in the Age of Large Language Models with NVIDIA FLARE, by Holger Roth (PDF)
10:00 – 10:30	Tea Break
10:30 – 11:00	Keynote 4: Uncertainty Quantification in Federated Learning, by Pascal Poupart
11:00 – 12:00	Oral Presentation Session (10 min per talk, including Q&A)
	Zehao Zhang, Guojun Zhang and Pascal Poupart. FAACL: Federated Adaptive Asymmetric Clustered Learning Xiaoli Tang, Han Yu and Xiaoxiao Li. A Bias Free Revenue Maximizing Bidding Strategy for Data Consumers in Auction based Federated Learning Long Teng. FedFSA:Fine-grained Secure Aggregation for Horizontal Federated Learning Haolin Yu, Guojun Zhang and Pascal Poupart. FedLog: Personalized Federated Classification with Less Communication and More Flexibility Qianyu Li, Xiaoli Tang, Han Yu and Hengjie Song. Personalized Federated Knowledge Graph Embedding with Multi-phase Client Training Haolin Yu, Kaiyang Guo, Mahdi Karami, Xi Chen, Guojun Zhang and Pascal Poupart. FedBNR: A Privacy-Friendly Global Federated Gaussian Process
12:00 – 12:30	Keynote 5: Networked AI Learning: A Non-Federated Learning Approach, by Wen Tong

Keynote Speakers

		Title: Federated Learning in Healthcare: Opportunities and Barriers Speaker: Ross Mitchell, Alberta Health Services (AHS) Chair in AI in Health, Professor in the Department of Medicine, and Adjunct professor in the Department of Computer Science, University of Alberta, Canada Biography Dr. Mitchell is the Alberta Health Services (AHS) Chair in AI in Health, a professor in the Department of Medicine, and an adjunct professor in the Department of Computer Science at the University of Alberta. He is also a fellow with the Alberta Machine Intelligence Institute and the senior program director of artificial intelligence adoption with AHS. He received his PhD at the University of Western Ontario and has been working in the fields of biomedical imaging, artificial intelligence, and machine learning for 30 years. Dr. Mitchell was the inaugural artificial intelligence officer at the H. Lee Moffitt Cancer Center and Research Institute in Tampa, Florida from 2019 to 2021. There he led efforts to develop AI tools to improve the efficiency and quality of cancer care, including models to predict patient outcomes from electronic health record data, and natural language processing to infer diagnostic codes from free-text pathology reports. He was a professor and the inaugural director of the Division of Medical Imaging Informatics in the Department of Radiology, Mayo Clinic in Arizona, from 2011 to 2019. He was a professor of Biomedical Engineering, Radiology, and Clinical Neurosciences at the University of Calgary from 2000 to 2011.
		Title: Building Multi-Foundation Agent Systems through Trustworthy Auction-based Federated Learning Speaker: Han Yu, Nanyang Assistant Professor, Nanyang Technological University, Singapore Biography Han Yu is a Nanyang Assistant Professor (NAP) in the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore. He has been a Visiting Scholar at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST) from 2017 to 2018. Between 2015 and 2018, he held the prestigious Lee Kuan Yew Post-Doctoral Fellowship (LKY PDF) at the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY). Before joining NTU, he worked as an Embedded Software Engineer at Hewlett-Packard (HP) Pte Ltd, Singapore. He obtained his PhD from the School of Computer Engineering, NTU in 2014. His work focuses on trustworthy federated ubiquitous learning (TrustFUL). He has published over 250 research papers in leading international conferences and journals. He co-authored the book Federated Learning - the first monograph on the topic of federated learning. His research work has been recognized with multiple scientific awards. In 2021, he co-founded the Trustworthy Federated Ubiquitous Learning (TrustFUL) Research Lab (https://trustful.federated-learning.org/). He is an Associate Editor of IEEE TNNLS, IJCAI Sponsorship Officer General, WWW'25 Sponsorship Co-Chair, and Vice Chair of the IEEE Computational Intelligence Society (CIS) Standards Committee. He is a Distinguished Member of CCF, and a Senior Member of AAAI and IEEE. For his continued contributions to the field of trustworthy AI and real-world impact in the society, he has been identified as one of the World's Top 2% Scientists in AI, and selected as one of the JCI Ten Outstanding Young Persons (TOYP) of Singapore.
		Title: Easy and Scalable Federated Learning in the Age of Large Language Models with NVIDIA FLARE Speaker: Holger Roth, Principal Federated Learning Scientist, NVIDIA, USA Biography Holger Roth, a Principal Federated Learning Scientist at NVIDIA, specializes in developing distributed and collaborative software and models for various industries using federated learning and analytics. He has been exploring the topic both from theoretical and practical standpoints. During the COVID-19 pandemic, he led the experimentation of a federated learning study involving twenty hospitals around the globe to train more generalizable models for predicting clinical outcomes in symptomatic patients. His other research interests include computer-assisted annotation, active learning, and natural language processing. He is an Associate Editor for IEEE Transactions of Medical Imaging and holds a Ph.D. from University College London, UK. In 2018, he was awarded the MICCAI Young Scientist Publication Impact Award.
		Title: Uncertainty Quantification in Federated Learning Speaker: Pascal Poupart, Professor, University of Waterloo, Canada Biography Pascal Poupart is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo, Waterloo (Canada). He is also a Canada CIFAR AI Chair at the Vector Institute and a member of the Waterloo AI Institute. He serves on the advisory board of the AI Institute For Advances in Optimization (2022-present). He served as Research Director and Principal Research Scientist at the Waterloo Borealis AI Research Lab funded by the Royal Bank of Canada (2018-2020). He also served as scientific advisor for ProNavigator (2017-2019), ElementAI (2017-2018) and DialPad (2017-2018). He received the B.Sc. in Mathematics and Computer Science at McGill University, Montreal (Canada) in 1998, the M.Sc. in Computer Science at the University of British Columbia, Vancouver (Canada) in 2000 and the Ph.D. in Computer Science at the University of Toronto, Toronto (Canada) in 2005. His research focuses on the development of algorithms for Machine Learning with application to Natural Language Processing and Material Design. He is most well known for his contributions to the development of Reinforcement Learning algorithms. Notable projects that his research team are currently working on include Bayesian federated learning, probabilistic deep learning, data efficient reinforcement learning, conversational agents, automated document editing, sport analytics, adaptive satisfiability and CO2 conversion & capture.
		Title: Networked AI Learning: A Non-Federated Learning Approach Speaker: Wen Tong, CTO, Huawei Wireless, Huawei Technologies Company Ltd., Ottawa, ON, Canada Biography Dr. Wen Tong is the CTO, Huawei Wireless and a Huawei Fellow. He is the head of Huawei wireless research, and the Huawei 5G chief scientist and led Huawei’s 10-year-long 5G wireless technologies research and development. Dr. Tong is the industry recognized leader in invention of advanced wireless technologies, Dr. Tong was elected as a Huawei Fellow and an IEEE Fellow. He was the recipient of IEEE Communications Society Industry Innovation Award in 2014, and IEEE Communications Society Distinguished Industry Leader Award for “pioneering technical contributions and leadership in the mobile communications industry and innovation in 5G mobile communications technology” in 2018. He is also the recipient of R.A. Fessenden Medal. For the past three decades, he had pioneered fundamental technologies from 1G to 6G wireless and WiFi with more than 550 awarded US patents. Prior to joining Huawei in 2009, Dr. Tong was the Nortel Fellow and head of the Network Technology Labs at Nortel. He joined the Wireless Technology Labs at Bell Northern Research in 1995 in Canada. Dr. Tong is a Fellow of the Canadian Academy of Engineering, a Fellow of the Royal Society of Canada, and a Fellow of the IEEE. Dr. Tong is based at Ottawa, Canada.

Accepted Papers

Xiaoli Tang, Han Yu and Xiaoxiao Li. A Bias Free Revenue Maximizing Bidding Strategy for Data Consumers in Auction based Federated Learning
Qianyu Li, Xiaoli Tang, Han Yu and Hengjie Song. Personalized Federated Knowledge Graph Embedding with Multi-phase Client Training
Long Teng. FedFSA:Fine-grained Secure Aggregation for Horizontal Federated Learning
Zehao Zhang, Guojun Zhang and Pascal Poupart. FAACL: Federated Adaptive Asymmetric Clustered Learning
Haolin Yu, Guojun Zhang and Pascal Poupart. FedLog: Personalized Federated Classification with Less Communication and More Flexibility
Haolin Yu, Kaiyang Guo, Mahdi Karami, Xi Chen, Guojun Zhang and Pascal Poupart. FedBNR: A Privacy-Friendly Global Federated Gaussian Process

Call for Papers

Foundation models (FMs) are typically associated with large language models (LLMs), like ChatGPT, and are characterized by their scale and broad applicability. While these models provide transformative capabilities, they also introduce significant challenges, particularly concerning distributed model management and related data privacy, efficiency, and scalability. The training of foundation models is data and resource intensive and the conventional methods are typically centralized; this creates significant challenges including regulatory and privacy concerns in real-world use cases. These include distributed training data, computational resources to manage distributed data repositories, and development of and alignment with regulatory guidelines (e.g., GDPR) that restrict sharing sensitive data.

Federated learning (FL) is an emerging paradigm that can mitigate these challenges by training a global but distributed model using distributed data. The extensive application of machine learning to analyze and draw insight from real-world, distributed, and sensitive data necessitates familiarity with and adoption of this relevant and timely topic within the general scientific community. As FL allows self-interested data owners to collaboratively train models, end-users can become co-creators of AI solutions. By adopting federated learning approaches, we can leverage distributed data and computing power available across different sources while respecting user privacy.

The rise of FMs amplifies the importance and relevance of FL as a crucial research direction. With FMs becoming the norm in machine learning development, the focus shifts from model architecture design to tackling the issues surrounding privacy-preserving and distributed learning. Advancements in FL methods have the potential to unlock the use of FMs, enabling efficient and scalable training while safeguarding sensitive data.

With this in mind, we invite original research contributions, position papers, and work-in-progress reports on various aspects of federated learning in the era of foundation models. Since the emergence of foundation models has been a relatively recent phenomenon, their full impact on federated learning has not yet been well explored or understood. We hope to provide a platform to facilitate interaction among students, scholars, and industry professionals from around the world to discuss the latest advancements, share insights, and identify future directions in this exciting field. The workshop topics include but are not limited to:

Theory and algorithmic foundations:

Impact of heterogeneity in FL of large models

Multi-stage model training (e.g., base model + fine tuning)

Optimization advances in FL (e.g., beyond first-order and local methods)

Prompt tuning in federated settings

Self-supervised learning in federated settings

Leveraging foundation models to improve federated learning:

Adaptive aggregation strategies for FL in heterogeneous environments

Foundation model enhanced FL knowledge distillation

Overcoming data interoperability challenges using foundation models

Personalization of FL with foundation models

Federated learning for training and tuning foundation models:

Fairness, bias, and interpretability challenges in FL with foundation models

Federated transfer learning with foundation models

FL techniques for traning large-scale foundation models

Hardware for FL with foundation models

Optimization algorithms for federated training of foundation models

Privacy-preserving mechanisms in FL with foundation models

Resource-efficient FL with foundation models

Security and robustness considerations in FL with foundation models

Systems and infrastructure for FL with foundation models

Vertical federated learning with foundation models

Vulnerabilities of FL with foundation models

More information on previous workshops can be found here.

Submission Instructions

Submitted papers must be written in English, with a maximum length limit of 6 printed pages. Papers that do not comply with the length limit will not be reviewed. Use the standard IEEE Transactions templates for Microsoft Word or LaTeX formats found at: https://www.ieee.org/conferences/publishing/templates.html All submitted papers will be under a single-blinded peer review for their novelty, technical quality and impact. The submissions can contain author details. Submission will be accepted via the Easychair submission website.

Easychair submission site: https://easychair.org/conferences/?conf=flfmicme2024

For enquiries, please email to: flfmicme2024@easychair.org