联邦基础模型中的十个挑战问题

IF 10.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tao Fan;Hanlin Gu;Xuemei Cao;Chee Seng Chan;Qian Chen;Yiqiang Chen;Yihui Feng;Yang Gu;Jiaxiang Geng;Bing Luo;Shuoling Liu;Win Kent Ong;Chao Ren;Jiaqi Shao;Chuan Sun;Xiaoli Tang;Hong Xi Tae;Yongxin Tong;Shuyue Wei;Fan Wu;Wei Xi;Mingcong Xu;He Yang;Xin Yang;Jiangpeng Yan;Hao Yu;Han Yu;Teng Zhang;Yifei Zhang;Xiaojin Zhang;Zhenzhe Zheng;Lixin Fan;Qiang Yang
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引用次数: 0

摘要

联邦基础模型(federfms)代表了一种分布式学习范式,它融合了基础模型的一般能力以及联邦学习的隐私保护能力。这种组合允许远程客户端的大型基础模型和小型本地领域模型在师生学习环境中相互学习。本文从基础理论、私有数据的利用、持续学习、取消学习、非iid和图形数据、双向知识转移、激励机制设计、博弈机制设计、模型水印和效率等方面全面总结了fedfm固有的十大挑战问题。这十个具有挑战性的问题体现在五个关键方面:“基础理论”,旨在为联邦货币基金组织建立一个连贯和统一的理论框架。“数据”,解决在维护隐私的同时利用来自私有数据的特定领域知识的困难;“异质性”,检查客户之间数据、模型和计算资源的变化;“安全与隐私”,专注于防御恶意攻击和模型盗窃;以及“效率”,强调在培训、沟通和参数效率方面需要改进。对于每个问题,我们都给出了目标函数的明确数学定义,分析了现有的方法,并讨论了主要挑战和潜在的解决方案。这一深入的探索旨在推进fedfm的理论基础,指导实际实现,并激发未来的研究来克服这些障碍,从而使fedfm在各种实际应用中具有鲁棒性、有效性和保密性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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