Ten Challenging Problems in Federated Foundation Models

Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the small local domain models at the remote clients to learn from each other in a teacher-student learning setting. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency. The ten challenging problems manifest in five pivotal aspects: “Foundational Theory,” which aims to establish a coherent and unifying theoretical framework for FedFMs. “Data,” addressing the difficulties in leveraging domain-specific knowledge from private data while maintaining privacy; “Heterogeneity,” examining variations in data, model, and computational resources across clients; “Security and Privacy,” focusing on defenses against malicious attacks and model theft; and “Efficiency,” highlighting the need for improvements in training, communication, and parameter efficiency. For each problem, we offer a clear mathematical definition on the objective function, analyze existing methods, and discuss the key challenges and potential solutions. This in-depth exploration aims to advance the theoretical foundations of FedFMs, guide practical implementations, and inspire future research to overcome these obstacles, thereby enabling the robust, efficient, and privacy-preserving FedFMs in various real-world applications.

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Publication Details

Journal
IEEE Transactions on Knowledge and Data Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tkde.2025.3555328
Citations
19
Primary Topic
Logic, Reasoning, and Knowledge
Type
article
Field-Weighted Citation Impact
47.14
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article

Ten Challenging Problems in Federated Foundation Models

Xuemei Cao, Yiqiang Chen, Bing Luo, Jiangpeng Yan et al.
19 citations
IEEE Transactions on Knowledge and Data Engineering
Logic, Reasoning, and Knowledge
47.14
article

Ten Challenging Problems in Federated Foundation Models

Xuemei Cao, Yiqiang Chen, Bing Luo, Jiangpeng Yan, Shuoling Liu, He Yang, Hanlin Gu, Yang Gu, Wei Xi, Zhenzhe Zheng, Yongxin Tong, Win Kent Ong, Fan Wu, Xin Yang, Chuan Sun, Han Yu, Chao Ren, Chee Seng Chan, Xiaoli Tang, Jiabei Shao, Teng Zhang, Lixin Fan, Yifei Zhang, Qiang Yang, Hao Yu, Qian Chen, Xiaojin Zhang, Tao Fan, Shuyue Wei, Yihui Feng, Jiaxiang Geng, Mingcong Xu, Hong Xi Tae
article en
19 citations

Abstract

Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the small local domain models at the remote clients to learn from each other in a teacher-student learning setting. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency. The ten challenging problems manifest in five pivotal aspects: “Foundational Theory,” which aims to establish a coherent and unifying theoretical framework for FedFMs. “Data,” addressing the difficulties in leveraging domain-specific knowledge from private data while maintaining privacy; “Heterogeneity,” examining variations in data, model, and computational resources across clients; “Security and Privacy,” focusing on defenses against malicious attacks and model theft; and “Efficiency,” highlighting the need for improvements in training, communication, and parameter efficiency. For each problem, we offer a clear mathematical definition on the objective function, analyze existing methods, and discuss the key challenges and potential solutions. This in-depth exploration aims to advance the theoretical foundations of FedFMs, guide practical implementations, and inspire future research to overcome these obstacles, thereby enabling the robust, efficient, and privacy-preserving FedFMs in various real-world applications.

IEEE Transactions on Knowledge and Data Engineering
Nanyang Technological University (SG), Shanghai Jiao Tong University (CN), Chinese Academy of Sciences (CN), Hong Kong University of Science and Technology (HK), Southwestern University of Finance and Economics (CN), University of Malaya (MY), Institute of Computing Technology (CN), Beijing Academy of Artificial Intelligence (CN), Duke Kunshan University (CN), Huazhong University of Science and Technology (CN), Beihang University (CN), KTH Royal Institute of Technology (SE), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 0%
Logic, Reasoning, and Knowledge
47.14
DOISourceOpenAlex
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