1. Xiaoli Tang and Han Yu. A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
2. Holger Roth, Ziyue Xu, Yuan-Ting Hsieh, Adithya Renduchintala, Isaac Yang, Zhihong Zhang, Emily Sakata, Yuhong Wen, Sean Yang, Kevin Lu, Kristopher Kersten, Camir Ricketts, Daguang Xu, Chester Chen, Yan Cheng and Andrew Feng. Easy and Scalable Federated Learning in the Age of Large Language Models with NVIDIA FLARE
3. Qianyu Li, Xiaoli Tang, Han Yu and Hengjie Song. Personalized Federated Knowledge Graph Embedding with Multi-phase Client Training
4. Long Teng. FedFSA:Fine-grained Secure Aggregation for Horizontal Federated Learning

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.

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

• 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)