VLMs have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infeasible. FL mitigates this issue by enabling decentralized training, but practical deployments face challenges due to client heterogeneity in computational resources, application requirements, and model architectures. We argue that while replacing data with model parameters characterizes the present of FL, replacing parameters with preferences represents a more scalable and privacy-preserving future. Motivated by this perspective, we propose MoR, a federated alignment framework based on GRPO with Mixture-of-Rewards for heterogeneous VLMs. MoR initializes a visual foundation model as a KL-regularized reference, while each client locally trains a reward model from local preference annotations, capturing specific evaluation signals without exposing raw data. To reconcile heterogeneous rewards, we introduce a routing-based fusion mechanism that adaptively aggregates client reward signals. Finally, the server performs GRPO with this mixed reward to optimize the base VLM. Experiments on three public VQA benchmarks demonstrate that MoR consistently outperforms federated alignment baselines in generalization, robustness, and cross-client adaptability. Our approach provides a scalable solution for privacy-preserving alignment of heterogeneous VLMs under federated settings.
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
Graduate School of AI, POSTECH, Pohang, Republic of Korea（POSTECH人工智能研究生院）；Department of CSE, POSTECH, Pohang, Republic of Korea（POSTECH计算机科学与工程系）；National AI Research Lab, Seoul, Republic of Korea（首尔国家人工智能研究实验室）
Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning
Institute of Engineering Research, Korea University（韩国大学工程研究所）；Kim Jaechul Graduate School of AI, KAIST（韩国科学技术院金在哲人工智能研究生院）；Industrial Management Engineering, Korea University（韩国大学产业管理工程学院）
MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Academy of Chinese Medical Sciences（广东省中医药科学院中医证候重点实验室，广州中医药大学第二附属医院，广东省中医院，广东省中医药科学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电子与计算机工程系）
Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness
AI Technology Center of OVB, Tencent（腾讯OVB人工智能技术中心）；Platform and Content Group, Tencent（腾讯平台与内容事业群）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
University of Houston（休斯顿大学）；Brown University（布朗大学）；University of Miami（迈阿密大学）；Nagoya University（名古屋大学）；University of Michigan（密歇根大学）；Northern Arizona University（北亚利桑那大学）
FedPOB: Sample-Efficient Federated Prompt Optimization via Bandits
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；South China University of Technology（华南理工大学）；University of Edinburgh（爱丁堡大学）；Beihang University（北航）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；East China Normal University（华东师范大学）；City University of Hong Kong（香港城市大学）
Mitigating Mismatch within Reference-based Preference Optimization
Sydney AI Centre, The University of Sydney（悉尼人工智能中心，悉尼大学）；CFAR, A*STAR（CFAR，A*STAR）；CSIRO, Data61（CSIRO，Data61）；Southeast University（东南大学）；Nanyang Technological University（南洋理工大学）
Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
Department of Computer Science and Engineering and the Research Institute of Trustworthy Autonomous Systems at Southern University of Science and Technology（计算机科学与工程系和可信自主系统研究 institute at 南方科技大学）
Personalized Recommendation Models in Federated Settings: A Survey
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）；Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity
Hong Kong JC STEM Lab of Smart City（香港JC STEM实验室）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Department of Electrical and Computer Engineering, The University of Hong Kong（香港大学电子与计算机工程系）
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning
George Mason University（乔治梅森大学）；The University of Hong Kong（香港大学）；Huazhong University of Science and Technology（华中科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The Hong Kong Polytechnic University（香港理工大学）
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
University of Electronic Science and Technology of China（电子科技大学）；Angelalign Technology Inc（时代天使科技有限公司）；Zhejiang University（浙江大学）；IHPC, Agency for Science, Technology and Research, Singapore（新加坡科技研究局信息通信研究院）；CFAR, Agency for Science, Technology and Research, Singapore（新加坡科技研究局计算科学与工程中心）；Singapore University of Technology and Design（新加坡科技设计大学）
