A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the summary of model performances across the training devices. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, which often results in learning models with poor generalization, i.e., an ability to handle new/unseen data, for real-world applications. Moreover, a hierarchical FL structure with distributed computing platforms demonstrates incoherent model performances at different aggregation levels. Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability. In this work, we adopt the novel democratized learning (Dem-AI) principles and designs to meet these objectives. Firstly, we show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn, as a practical framework to empower generalization capability in support of FA. Secondly, we validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions by leveraging the distributed computing infrastructure. The distributed edge computing servers construct regional models, minimize the communication loads, and ensure distributed data analytic application's scalability. To that end, we adhere to a near-optimal two-sided many-to-one matching approach to handle the combinatorial constraints in Edge-DemLearn and solve it for fast knowledge acquisition with optimization of resource allocation and associations between multiple servers and devices. Extensive simulation results on real datasets demonstrate the effectiveness of the proposed methods.
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
The University of Danang - Vietnam-Korea University of Information and Communication Technology（岘港大学-越韩信息与通信技术大学）；Kyung Hee University（庆熙大学）；Aalborg University（奥尔堡大学）；The University of Sydney（悉尼大学）
Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins
Department of Electrical and Computer Engineering, McMaster University, CANADA（电气与计算机工程系，麦斯特大学）；Department of Computing and Software, McMaster University, CANADA（计算与软件系，麦斯特大学）
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, State Key laboratory of Space Network and Communications, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系、空间网络与通信国家重点实验室、北京信息科学与技术国家研究中心）；Department of Computer Science, Brunel University（布鲁内尔大学计算机科学系）；Department of Electronic Engineering, Shanghai JiaoTong University（上海交通大学电子工程系）；School of Internet of Things Engineering, Wuxi Institute of Technology（无锡职业技术学院物联网工程学院）
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China（电子科技大学智能组网与通信中心）
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Group（阿里巴巴集团）；The Pennsylvania State University（宾夕法尼亚州立大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（信息与通信工程学院，北京邮电大学）；School of Computing, Macquarie University（计算学院，麦考瑞大学）；Department of Broadband Communication, Peng Cheng Laboratory（宽带通信部，鹏城实验室）；China Telecom, Sichuan Branch（中国电信四川分公司）
Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning
Broadband Access Network Laboratory, Shanghai Jiao Tong University（宽带接入网络实验室，上海交通大学）；School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；Data61, CSIRO（数据61，联邦科学与工业研究组织）；Department of Electronic and Electrical Engineering, University College London（电子与电气工程系，伦敦大学学院）；School of Internet of Things Engineering, Jiangnan University（物联网工程学院，江南大学）
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
School of Intelligent Software and Engineering, Nanjing University（南京大学智能软件与工程学院）；School of Information Science and Engineering, Hunan Normal University（湖南师范大学信息科学与工程学院）；Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business（湖南工商大学长沙人工智能社会实验室）；Brunel University London（布鲁内尔伦敦大学）
Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise
School of Data and Sciences（数据与科学学院）；BRAC University（布拉克大学）；Electrical and Computer Engineering（电气与计算机工程）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Electrical and Electronic Engineering（电气与电子工程）；Bangladesh University Engineering and Technology（孟加拉工程与技术大学）
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring
Global AI Accelerator, Ericsson（爱立信全球人工智能加速器）；Ericsson Research（爱立信研究院）
各种医疗保健应用，如辅助生活、跌倒检测等，需要通过人类活动识别（HAR）对用户行为进行建模。这样的应用需要使用机器学习技术来表征来自多个资源受限的用户设备的见解，以用于有效的个性化活动监视。基于设备的联邦学习被证明是分布式协作机器学习的一种有效方法。然而，在解决用户之间的统计（非IID数据）和模型异质性方面存在各种挑战。此外，在本文中，我们探索了一个新的挑战-在联邦学习过程中处理用户之间标签（活动）的异质性。为此，我们提出了一个框架，联邦标签为基础的聚合，利用重叠的信息增益跨活动使用模型蒸馏更新。我们还建议，模型分数的联邦传输是足够的，而不是从设备到服务器的模型权重传输。在Raspberry Pi 2上使用Heteroimmune Human Activity Recognition（HHAR）数据集（有四个活动用于有效阐明结果）进行的实证评估表明，平均确定性准确度至少提高了~ 11.01%，从而证明了我们提出的框架的设备上功能。
University of Louisville（路易斯维尔大学）；North Carolina State University（北卡罗来纳州立大学）；University of Wisconsin-Whitewater（威斯康星大学怀特沃特分校）；Wayne State University（韦恩州立大学）；The Ohio State University（俄亥俄州立大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Duke University（杜克大学）
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
Department of Electrical Engineering, Chalmers University of Technology（楚德大学电气工程系）；School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of New South Wales（新南威尔士大学）；Weizmann Institute of Science（魏茨曼科学研究所）；Princeton University（普林斯顿大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of Trustworthy Autonomous Systems（深圳市可信自主系统研究院）；University of Macau（澳门大学）
