In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further scale up, it is crucial to incorporate collaboration among multiple specialized and high-quality private domain data sources. However, the challenge of training models locally without sharing private data presents numerous obstacles in data quality control. To tackle this issue, we propose a data quality control pipeline for federated fine-tuning of foundation models. This pipeline computes scores reflecting the quality of training data and determines a global threshold for a unified standard, aiming for improved global performance. Our experiments show that the proposed quality control pipeline facilitates the effectiveness and reliability of the model training, leading to better performance.
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models
College of Software, Jilin University（吉林大学软件学院）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；College of Computer Science and Engineering, Changchun University of Technology（长春理工大学计算机科学与工程学院）
Enhancing Robustness of Federated Learning via Server Learning
National Institute of Standards and Technology (NIST)（美国国家标准与技术研究院）；University of Maryland, College Park（马里兰大学帕克分校）；Tata Consultancy Services Research（塔塔咨询服务公司研究院）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
Australian Artificial Intelligence Institute, University of Technology Sydney（澳大利亚人工智能研究所，悉尼技术大学）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
Synthetic Data Aided Federated Learning Using Foundation Models
The University of Manchester（曼彻斯特大学）；Institute for Infocomm Research, A*STAR（新加坡资讯通信研究院（新加坡科技研究局下属机构））；KU Leuven（鲁汶大学）；COSIC, KU Leuven（鲁汶大学计算机与安全工业密码学中心）
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Future Network of Intelligence Institute（未来智联网络研究院）；Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来智联网络重点实验室）
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
Tsinghua University（清华大学）；Microsoft Research（微软研究院）；Idiap Research Institute（Idiap研究所）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；Chinese Academy of Sciences（中国科学院）；Hong Kong University of Science and Technology（香港科技大学）
Fairness-Aware Client Selection for Federated Learning
Nanyang Technological University (NTU)（南洋理工大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究院）；Alibaba Group（阿里巴巴集团）；University of Science and Technology of China（中国科学技术大学）
Enhancing Cross-Problem Vehicle Routing via Federated Learning
Dalian University of Technology（大连理工大学）；Nanyang Technological University（南洋理工大学）；Delft University of Technology（代尔夫特理工大学）；Eindhoven University of Technology（埃因霍温理工大学）
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training
Department of Industrial Design, Eindhoven University of Technology（工业设计系，埃因霍温理工大学）；Department of Arts & Sciences, College of William & Mary（文理学院，威廉与玛丽学院）
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
School of Computing, Southern Illinois University（南伊利诺伊大学计算机学院）；Security, Privacy and Intelligence for Edge Devices Laboratory (SPEED Lab)（边缘设备安全、隐私与智能实验室（SPEED Lab））
Discrete Prototypical Memories for Federated Time Series Foundation Models
Australian Artificial Intelligence Institute, University of Technology Sydney（澳大利亚人工智能研究所，悉尼科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shenzhen University（深圳大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Sichuan University（四川大学）
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
School of Engineering and Built Environment, Deakin University（迪肯大学工程与建筑环境学院）；School of Electrical Engineering and Computer Science, The University of Queensland（昆士兰大学电气工程与计算机科学学院）；School of Information Technology, Deakin University（迪肯大学信息技术学院）
