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Ten Challenging Problems in Federated Foundation Models

文献类型：期刊论文

作者Fan, Tao2; Gu, Hanlin2; Cao, Xuemei3; Chan, Chee Seng4; Chen, Qian5; Chen, Yiqiang5; Feng, Yihui3; Gu, Yang5; Geng, Jiaxiang6; Luo, Bing6
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2025-07-01
卷号37期号:7页码:4314-4337
关键词Frequency modulation Foundation models Privacy Optimization Adaptation models Watermarking Knowledge transfer Training Fans Data privacy Federated foundation models (FedFMs) federated learning foundation models large language models privacy-preserving Ai
ISSN号1041-4347
DOI10.1109/TKDE.2025.3555328
英文摘要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.
资助项目National Natural Science Foundation of China[62476228] ; National Natural Science Foundation of China[62425202] ; Suzhou Frontier Science and Technology Program[SYG202310] ; Ministry of Education, Singapore, under its Academic Research Fund Tier1 ; Ministry of Higher Education, Malaysia, through Fundamental Research Grant Scheme[FRGS/1/2024/ICT02/UM/01/1]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001504151700028
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/42337]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yang, Qiang
作者单位1.Hong Kong Polytech Univ, Acad Artificial Intelligence, Kowloon, Hong Kong, Peoples R China
2.WeBank, Shenzhen 518000, Peoples R China
3.Southwestern Univ Finance & Econ, Chengdu 610074, Peoples R China
4.Univ Malaya, Kuala Lumpur 50603, Malaysia
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China
6.Duke Kunshan Univ, Kunshan 215316, Peoples R China
7.Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China
8.Xi An Jiao Tong Univ, Xian 710049, Peoples R China
9.Huazhong Univ Sci & Technol, Wuhan 430074, Peoples R China
10.E Fund Management Co Ltd, Inst Innovat, Guangzhou 510620, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Tao,Gu, Hanlin,Cao, Xuemei,et al. Ten Challenging Problems in Federated Foundation Models[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2025,37(7):4314-4337.
APA Fan, Tao.,Gu, Hanlin.,Cao, Xuemei.,Chan, Chee Seng.,Chen, Qian.,...&Yang, Qiang.(2025).Ten Challenging Problems in Federated Foundation Models.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,37(7),4314-4337.
MLA Fan, Tao,et al."Ten Challenging Problems in Federated Foundation Models".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 37.7(2025):4314-4337.

入库方式： OAI收割

来源：计算技术研究所

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