Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling to millions of devices across various learning domains while offering meaningful differential privacy (DP) guarantees. Production systems from organizations like Google, Apple, and Meta demonstrate the real-world applicability of FL. However, key challenges remain, including verifying server-side DP guarantees and coordinating training across heterogeneous devices, limiting broader adoption. Additionally, emerging trends such as large (multi-modal) models and blurred lines between training, inference, and personalization challenge traditional FL frameworks. In response, we propose a redefined FL framework that prioritizes privacy principles rather than rigid definitions. We also chart a path forward by leveraging trusted execution environments and open-source ecosystems to address these challenges and facilitate future advancements in FL.
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
School of Cyber Engineering, Xidian University（电子科技大学信息工程学院）；RMIT University（皇家墨尔本理工大学）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（澳大利亚联邦科学与工业研究组织）；UNSW Sydney（新南威尔士大学悉尼分校）；University of California, Davis（加州大学戴维斯分校）
FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
Carnegie Mellon University（卡内基梅隆大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Avignon（阿维尼昂大学）；Singapore University of Technology and Design（新加坡科技与设计大学）；University of Washington（华盛顿大学）
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；International School, Beijing University of Posts and Telecommunications（北京邮电大学国际学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
School of Information Management and Engineering, Shanghai University of Finance and Economics（上海金融学院信息管理与工程学院）；School of Information Management and Engineering, Shanghai University of Finance and Economics（上海金融学院信息管理与工程学院；明尼苏达大学工业与系统工程系）；Department of Industrial and System Engineering, University of Minnesota（明尼苏达大学电气与计算机工程系）；Department of Electrical and Computer Engineering, University of Minnesota
Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach
Colleague of Computer Science and Technology, National University of Defense Technology, Changsha, China（计算机科学与技术系，国防科技大学，中国长沙）；City University of Hong Kong, Hong Kong, China（香港城市大学，中国香港）；Shenzhen University, Shenzhen, China（深圳大学，中国深圳）
FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
School of Computing, KAIST（韩国科学技术院计算机学院）；Samsung Research（三星研究院）；Data Science Group, Institute for Basic Science（基础科学研究院数据科学组）；Max Planck Institute for Security and Privacy（马克斯·普朗克安全与隐私研究所）
GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS
School of Software Engineering, Nanjing University of Information Science and Technology（南京信息工程大学软件学院）；College of Meteorology and Oceanography, National University of Defense Technology（国防科技大学气象海洋学院）；CSIRO’s Data61（澳大利亚CSIRO数据61研究所）；Medical Sciences Division, University of Oxford（牛津大学医学科学学院）；Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services（江苏省先进计算与智能服务工程研究中心）；Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology（江苏省大气环境与装备技术协同创新中心）；Nanjing University of Information Science and Technology（南京信息工程大学）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement
Institute of Computing Technology, Chinese Academy of Sciences, China（中国科学院计算技术研究所）；Purple Mountain Laboratories, China（紫金山实验室）；University of Chinese Academy of Sciences, China（中国科学院大学）
A Step Toward Federated Pretraining of Multimodal Large Language Models
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统实验室）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Pengcheng Laboratory（鹏城实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）
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）
Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所）；University of Helsinki（赫尔辛基大学）；Waseda University（早稻田大学）；Aalto University（阿尔托大学）；University of Manchester（曼彻斯特大学）
