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: July 15, 2024
Venue: Room Peninsula, Marriott Convention & Exhibition Centre, Niagara Falls, ON, Canada

Keynote Speakers

   

Title: TBA

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: TBA

Speaker: Qiang Yang, Chief AI Officer (CAIO), WeBank / Professor Emeritus, Hong Kong University of Science and Technology (HKUST), Hong Kong, China

Biography
Qiang Yang is the head of the AI Department at WeBank (Chief AI Officer) and Professor Emeritus at the Computer Science and Engineering (CSE) Department of the Hong Kong University of Science and Technology (HKUST), where he was a former head of CSE Department and founding director of the Big Data Institute (2015-2018). His research interests include AI, machine learning, and data mining, especially in transfer learning, automated planning, federated learning, and case-based reasoning. He is a fellow of several international societies, including ACM, AAAI, IEEE, IAPR, and AAAS. He received his Ph.D. in Computer Science in 1989 and his M.Sc. in Astrophysics in 1985, both from the University of Maryland, College Park. He obtained his B.Sc. in Astrophysics from Peking University in 1982. He had been a faculty member at the University of Waterloo (1989-1995) and Simon Fraser University (1995-2001). He was the founding Editor-in-Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and IEEE Transactions on Big Data (IEEE TBD). He served as the President of International Joint Conference on AI (IJCAI, 2017-2019) and an executive council member of Association for the Advancement of AI (AAAI, 2016-2020). Qiang Yang is a recipient of several awards, including the 2004/2005 ACM KDDCUP Championship, the ACM SIGKDD Distinguished Service Award (2017), and AAAI Innovative Application Awards (2018, 2020 and 2022). He was the founding director of Huawei's Noah's Ark Lab (2012-2014) and a co-founder of 4Paradigm Corp, an AI platform company. He is an author of several books including Intelligent Planning (Springer), Crafting Your Research Future (Morgan & Claypool), and Constraint-based Design Recovery for Software Engineering (Springer).

   

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.


Accepted Papers

  1. Xiaoli Tang and Han Yu. A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
  2. Qianyu Li, Xiaoli Tang, Han Yu and Hengjie Song. Personalized Federated Knowledge Graph Embedding with Multi-phase Client Training
  3. Long Teng. FedFSA:Fine-grained Secure Aggregation for Horizontal Federated Learning

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


Organizing Committee

General Co-Chairs         Program Co-Chairs
   


Randy Goebel
(U Alberta)
   

Xiaoxiao Li
(UBC)
       

Han Yu
(NTU)
   

Jane Z. Wang
(UBC)
   

Ross Mitchell
(U Alberta)
   

Program Committee

  • Alysa Ziying Tan (Nanyang Technological University)
  • Guojun Zhang (Noah's Ark Lab)
  • Jihong Park (Deakin University)
  • Jun Luo (Noah's Ark Lab)
  • Liping Yi (Nankai University)
  • Siwei Feng (Soochow University)
  • Wenlong Deng (The University of British Columbia)
  • Xi Chen (Noah's Ark Lab)
  • Xianjie Guo (Hefei University of Technology)
  • Xiaoli Tang (Nanyang Technological University)
  • Yanci Zhang (Shandong University)
  • Yuxin Shi (Nanyang Technological University)
  • Zhuan Shi (Swiss Federal Institute of Technology Lausanne)
  • Zichen Chen (University of California Santa Barbara)

Organized by