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Advances and Open Challenges in Federated Foundation Models

Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Bo Zhao, Liping Yi, Alysa Ziying Tan, Yulan Gao, Anran Li, Xiaoxiao Li, Zengxiang Li, Qiang Yang

TL;DR

A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization, and offering a blueprint for future research directions, emphasizing the need for developing trustworthy solutions.

Abstract

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field.

Advances and Open Challenges in Federated Foundation Models

TL;DR

A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization, and offering a blueprint for future research directions, emphasizing the need for developing trustworthy solutions.

Abstract

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field.
Paper Structure (63 sections, 14 figures, 10 tables, 1 algorithm)

This paper contains 63 sections, 14 figures, 10 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation
  3. Related Works and Contributions
  4. Organization of This Survey Paper
  5. Background
  6. Foundation Models
  7. Federated Learning
  8. The Proposed FedFM Taxonomy
  9. Efficient Federated Training and Aggregation: The Foundation of FedFM
  10. Trustworthiness: A Crucial Pillar of FedFM Integrity
  11. Incentive Mechanisms: Fostering Participation, Collaboration, and Adaptability of FedFM
  12. Summary of the Proposed FedFM Taxonomy
  13. Towards Efficient FedFM
  14. FedFM Training and Aggregation
  15. Scaling Efforts for FedFM
  16. ...and 48 more sections

Figures (14)

  • Figure 1: Organization of the FedFM survey.
  • Figure 2: The proposed taxonomy of FedFM.
  • Figure 3: Taxonomy of enabling techniques for efficient FedFM. The diagram categorizes existing methods into three main domains: 1) Aggregation, 2) Computational Efficiency, and 3) Communication Efficiency. Each domain is further divided into specific strategies that address the challenges of FedFM.
  • Figure 4: Illustration of efficient FedFM.
  • Figure 5: Illustration of weight averaging strategy of aggregation methods in FedFM.
  • ...and 9 more figures