Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both machine learning and network programming. This paper presents a comprehensive solution that simplifies the orchestration of FL tasks while integrating intent-based automation. We develop a user-friendly web application supporting the federated averaging (FedAvg) algorithm, enabling users to configure parameters through an intuitive interface. The backend solution efficiently manages communication between the parameter server and edge nodes. We also implement model compression and scheduling algorithms to optimize FL performance. Furthermore, we explore intent-based automation in FL using a fine-tuned Language Model (LLM) trained on a tailored dataset, allowing users to conduct FL tasks using high-level prompts. We observe that the LLM-based automated solution achieves comparable test accuracy to the standard web-based solution while reducing transferred bytes by up to 64% and CPU time by up to 46% for FL tasks. Also, we leverage the neural architecture search (NAS) and hyperparameter optimization (HPO) using LLM to improve the performance. We observe that by using this approach test accuracy can be improved by 10-20% for the carried out FL tasks.
Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
Josef Ressel Centre for Intelligent and Secure Industrial Automation（乔塞夫·雷斯尔智能与安全工业自动化中心）；Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）；Department of Artificial Intelligence and Human Interfaces（人工智能与人机接口部门）；Paris Lodron University of Salzburg（萨尔茨堡巴黎洛登大学）
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing
Iquadrat Informatica S.L.（伊夸德拉特信息有限公司）；Technical University of Catalonia (UPC)（加泰罗尼亚理工大学）；i2CAT Foundation（i2CAT基金会）；ATHENA/ISI（雅典娜信息系统研究所）；University of Patras（帕特雷大学）
VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture
Department of Electrical and Computer Engineering, The University of Alabama（电气与计算机工程系，阿拉巴马大学）；Electrical and Computer Engineering, Michigan State University（电气与计算机工程，密歇根州立大学）；Agricultural and Biological Engineering, Mississippi State University（农业与生物工程，密苏里州立大学）；Department of Computer Science, University of Alabama（计算机科学系，阿拉巴马大学）；USDA-ARS Genetics and Sustainbale Agriculture（美国农业部ARS基因与可持续农业）
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence
Huazhong University of Science and Technology（华中科技大学）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））；China University of Geosciences（中国地质大学）；Xidian University（西安电子科技大学）；University of Arizona（亚利桑那大学）；University of Technology Sydney（悉尼科技大学）
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
SAMOVAR, Télécom SudParis（SAMOVAR，法国电信南巴黎学院）；Department of Computer Science, German University of Technology in Oman (GUtech)（阿曼技术大学计算机科学系）；L3i, La Rochelle University（拉罗什大学L3i）
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
WeBank（微众银行）；Hong Kong University of Science and Technology（香港科技大学）；Extreme Vision Ltd（极视角有限公司）；Nanyang Technological University（南洋理工大学）；Joint NTU-WeBank Research Centre on FinTech（南大-微众金融科技联合研究中心）
Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation
School of Computer Science and Engineering（计算机科学与工程学院）；Vellore Institute of Technology（韦洛尔理工学院）
Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
Morfix；OpenMined；École Supérieure en Informatique, Sidi Bel Abbès（西迪贝勒阿巴斯高等计算机学院）；Bitdefender（比特梵德）；Edinburgh Napier University（爱丁堡龙比亚大学）；apheris AI；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Tessella
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）
