Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local training schedules, normalization, regularization, and model architecture. These choices are expensive to explore manually and difficult to compare fairly when candidate changes can also alter the FL training or evaluation path. In this work, we present Auto-FL-Research (AFR), a constrained coding-agent workflow for FL algorithmic recipe search. Agents may propose and implement candidate training algorithms, including server aggregation rules, client update schedules, local objectives, and registered model variants, while task profiles fix the mutation surface, compute budget, communication contract, and final model evaluation. Each campaign records candidate scores, runtime, edited files, artifacts, and failure status. We evaluate AFR on five healthcare cross-silo FLamby tasks and on grouped-client profiles for the five fixed LEAF datasets plus the LEAF synthetic task. Five-seed repeat evaluations support gains on four FLamby tasks and five of six LEAF profiles, while also exposing seed-sensitive and search-selected failure cases. Same-budget controls show that several gains correspond to FL-recipe changes, whereas other improvements are recovered by fixed-surface scalar controls or fail under repeat or held-out evaluation. These mixed outcomes are part of the contribution: they show how agent-generated candidates can be separated into repeated FL mechanisms, fixed-surface tuning effects, and selected single-run artifacts.
Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications
Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology（6G移动通信研究中心，信息科学与工程学院，华中科技大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；School of Engineering, Edith Cowan University（工程学院，埃迪斯·科文大学）；School of Computer Science and Engineering, University of New South Wales (UNSW)（计算机科学与工程学院，新南威尔士大学）；School of Electrical and Computer Engineering, University of Sydney（电气与计算机工程学院，悉尼大学）；Graduate School of Information Sciences, Tohoku University（信息科学研究生院，东北大学）
From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada（计算机科学与人工智能系，数据科学与计算智能安达卢西亚研究 institute，格拉纳达大学）；Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada（软件工程系，数据科学与计算智能安达卢西亚研究 institute，格拉纳达大学）
Agentic Federated Learning: The Future of Distributed Training Orchestration
Institute of Computing, University of Campinas（坎皮纳斯州立大学计算研究所）；Hub of Artificial Intelligence and Cognitive Architectures - H.IAAC（人工智能与认知架构中心 - H.IAAC）
Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks
London South Bank University（伦敦南岸大学）；Instituto de Telecomunicações（电信研究所）
Diamond Agent: Agentic Control of Federated HPC Resources as a Service
Rutgers University（罗格斯大学）；University of Rochester（罗切斯特大学）；National Center for Supercomputing Applications（国家超级计算应用中心）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）
LAFA: Agentic LLM-Driven Federated Analytics over Decentralized Data Sources
Shanghai Jiao Tong University（上海交通大学）；Peng Cheng Laboratory（鹏城实验室）；The University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Houston（休斯顿大学）
AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Beijing University of Posts and Telecommunications（北京邮电大学）；Guangxi Transportation Science and Technology Group Co., Ltd.（广西交通科技集团有限公司）；Fudan University（复旦大学）
A Sober Look at Agentic Misalignment in Automated Workflows
University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Utah（犹他大学）；University of Notre Dame（圣约翰大学）；George Washington University（乔治华盛顿大学）
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Tongyi Lab , Alibaba Group（通义实验室，阿里巴巴集团）；Alibaba Group（阿里巴巴集团）；SUAT（深圳大学）
An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design
Department of Electrical and Computer Engineering, Duke University, Durham, USA（电气与计算机工程系，杜克大学，达勒姆，美国）；Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, USA（电气工程与计算机科学系，密苏里大学，哥伦比亚，美国）
SW-$A^2$-Bench: Benchmarking Autonomous Software Agent Generation for Agentic Web
Shanghai Jiao Tong University（上海交通大学）；The University of Tokyo（东京大学）；Huazhong University of Science and Technology（华中科技大学）；Shanghai Innovation Institute（上海创新研究院）；Nankai University（南开大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Queen’s University（女王大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）
