When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

• URL: http://arxiv.org/abs/2306.15546v2
• Date: Mon, 1 Jan 2024 13:07:10 GMT
• Title: When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
• Authors: Weiming Zhuang, Chen Chen, Lingjuan Lyu
• Abstract summary: The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual benefits. FL expands the availability of data for FMs and enables computation sharing, distributing the training process and reducing the burden on FL participants. On the other hand, FM, with its enormous size, pre-trained knowledge, and exceptional performance, serves as a robust starting point for FL.
• Score: 47.00147534252281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual benefits, presents a unique opportunity to unlock new possibilities in AI research, and address critical challenges in AI and real-world applications. FL expands the availability of data for FMs and enables computation sharing, distributing the training process and reducing the burden on FL participants. It promotes collaborative FM development, democratizing the process and fostering inclusivity and innovation. On the other hand, FM, with its enormous size, pre-trained knowledge, and exceptional performance, serves as a robust starting point for FL, facilitating faster convergence and better performance under non-iid data. Additionally, leveraging FM to generate synthetic data enriches data diversity, reduces overfitting, and preserves privacy. By examining the interplay between FL and FM, this paper aims to deepen the understanding of their synergistic relationship, highlighting the motivations, challenges, and future directions. Through an exploration of the challenges faced by FL and FM individually and their interconnections, we aim to inspire future research directions that can further enhance both fields, driving advancements and propelling the development of privacy-preserving and scalable AI systems.

Related papers

• Federated Large Language Models: Current Progress and Future Directions [63.68614548512534]
  This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
  arXiv  Detail & Related papers  (2024-09-24T04:14:33Z)
• Synergizing Foundation Models and Federated Learning: A Survey [23.416321895575507]
  This paper discusses the potentials and challenges of synergizing Federated Learning (FL) and Foundation Models (FM) FL is a collaborative learning paradigm that breaks the barrier of data availability from different participants. It provides a promising solution to customize and adapt FMs to a wide range of domain-specific tasks using distributed datasets whilst preserving privacy.
  arXiv  Detail & Related papers  (2024-06-18T17:58:09Z)
• Advances and Open Challenges in Federated Foundation Models [34.37509703688661]
  The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI) This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM)
  arXiv  Detail & Related papers  (2024-04-23T09:44:58Z)
• A Survey on Efficient Federated Learning Methods for Foundation Model Training [62.473245910234304]
  Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications.
  arXiv  Detail & Related papers  (2024-01-09T10:22:23Z)
• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights [52.024964564408]
  This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack. It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
  arXiv  Detail & Related papers  (2023-12-07T20:39:57Z)
• The Role of Federated Learning in a Wireless World with Foundation Models [59.8129893837421]
  Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. Currently, the exploration of the interplay between FMs and federated learning (FL) is still in its nascent stage. This article explores the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities.
  arXiv  Detail & Related papers  (2023-10-06T04:13:10Z)
• Deep Equilibrium Models Meet Federated Learning [71.57324258813675]
  This study explores the problem of Federated Learning (FL) by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning.
  arXiv  Detail & Related papers  (2023-05-29T22:51:40Z)
• Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models [8.184714897613166]
  We propose the Federated Foundation Models (FFMs) paradigm, which combines the benefits of FMs and Federated Learning (FL) We discuss the potential benefits and challenges of integrating FL into the lifespan of FMs, covering pre-training, fine-tuning, and application. We explore the possibility of continual/lifelong learning in FFMs, as increased computational power at the edge may unlock the potential for optimizing FMs using newly generated private data close to the data source.
  arXiv  Detail & Related papers  (2023-05-19T03:51:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.