Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage and security threats if the data must be collected from different devices. To address the above issue, this paper proposes a federated data-driven evolutionary optimization framework that is able to perform data driven optimization when the data is distributed on multiple devices. On the basis of federated learning, a sorted model aggregation method is developed for aggregating local surrogates based on radial-basis-function networks. In addition, a federated surrogate management strategy is suggested by designing an acquisition function that takes into account the information of both the global and local surrogate models. Empirical studies on a set of widely used benchmark functions in the presence of various data distributions demonstrate the effectiveness of the proposed framework.
Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices. However, compared with the traditional centralized approach, the federated setting consumes considerable communication resources of the clients, which is indispensable for updating global models and prevents this technique from being widely used. In this paper, we aim to opti…
Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Zhejiang Lab（浙江实验室）；University of Manitoba（曼尼托巴大学）；Macau University of Science and Technology（澳门科学理工学院）
FedCross: Intertemporal Federated Learning Under Evolutionary Games
Wuhan University of Science and Technology（武汉科技大学）；Dalian University of Technology（大连理工大学）；Ministry of Education（教育部）；Zhejiang Normal University（浙江师范大学）
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Sardar Patel Institute of Technology（萨达尔·帕特尔理工学院）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smar…
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare
Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科学与技术大学）；Department of Mathematical Sciences, University of Alabama in Huntsville（数学科学系，阿拉巴马大学亨茨维尔分校）
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
University of Michigan（密歇根大学）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）；American University of Beirut（贝鲁特美国大学）；National University of Singapore（新加坡国立大学）
University of Padova（帕多瓦大学）；Politecnico di Torino（都灵理工大学）
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data. However, most of the existing works on FL unrealistically assume labeled data in the remote clients. Here we propose a novel task (FFREEDA) in which the clients' data is unlabeled and the server accesses a source labeled dataset for pre-training only. To solve FFREEDA, we propose LADD, which leverages the knowledge of the pre-trained model by employing self-supervision with ad-hoc regularization techniq…
Federated Learning allows training of data stored in distributed devices without the need for centralizing training data, thereby maintaining data privacy. Addressing the ability to handle data heterogeneity (non-identical and independent distribution or non-IID) is a key enabler for the wider deployment of Federated Learning. In this paper, we propose a novel Divide-and-Conquer training methodology that enables the use of the popular FedAvg aggregation algorithm by overcoming the acknowledged FedAvg limitations in non-IID environments. We propose a novel use of Cosine-distance based Weight Di…
Entropy-driven Fair and Effective Federated Learning
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工程学院）；School of Science Information and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）
Federated Learning for Data-Driven Feedforward Control: A Case Study on Vehicle Lateral Dynamics
Center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH（视觉、自动化与控制中心，奥地利技术研究院）；Robert Bosch GmbH（博世集团）；Automation and Control Institute, TU Wien（自动化与控制研究所，维也纳技术大学）
Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures. They bring great convenience to the use of GCN, but could hardly apply to the Federated Learning (FL) scenarios with distributed and private datasets, which limit their applications. Moreover, they need to train many candidate GCN models from scratch, which is inefficient for FL. To address these challenges, we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios. FL-AGCNS designs a federated evolutionary optimization strateg…
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
ENSIAS, Mohammed V University in Rabat（拉巴特穆罕默德五世大学ENSIAS学院）；Higher School of Technology, Hassan II University in Casablanca（卡萨布兰卡哈桑二世大学高等技术学院）；School of Science and Engineering, Al Akhawayn University in Ifrane（伊夫兰阿卡韦恩大学科学与工程学院）
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare
Purdue University（普渡大学）；Davidson School of Chemical Engineering（戴维森化学工程学院）；Research Institute for Advanced Computer Science（高级计算机科学研究所）；Universities Space Research Association（大学空间研究协会）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉法叶分校计算与信息学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；University of Delaware（特拉华大学）
FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays
University of Asia Pacific（亚太大学）；University of North Texas（北得克萨斯大学）；Green University of Bangladesh（孟加拉国绿色大学）；Southern Illinois University Carbondale（南伊利诺伊大学卡本代尔分校）
Politecnico di Torino（都灵理工大学）；University of Tübingen（蒂宾根大学）；Sapienza University of Rome（罗马第一大学）；Politecnico di Milano（米兰理工大学）；Italian Institute of Technology（意大利理工学院）
Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The central model has no direct access to the data, but only to the updates of the parameters computed locally by each client. This raises a problem, known as statistical heterogeneity, because the clients may have different data distributions (i.e. domains). This is only partly alleviated by clustering the clients. Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the da…
In this paper, we show how the Federated Learning (FL) framework enables learning collectively from distributed data in connected robot teams. This framework typically works with clients collecting data locally, updating neural network weights of their model, and sending updates to a server for aggregation into a global model. We explore the design space of FL by comparing two variants of this concept. The first variant follows the traditional FL approach in which a server aggregates the local models. In the second variant, that we call Flow-FL, the aggregation process is serverless thanks to …
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；SRIBD（深圳大数据研究院）；Wuhan University（武汉大学）
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used learning models, namely the deep neural network model and the Gaussian process model, and (3) various distributed model hyper-parameter optimization schemes. Then, we demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works, which c…
ITIS Software, University of Malaga（马拉加大学ITIS软件研究所）；University of Constantine 2（君士坦丁第二大学）
Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication load, which jeopardises the functioning efficiency. The difficulty of reducing this overhead stands in achieving this without decreasing the model's efficiency (contradictory relation). To do so, many works investigated the compression of the pre/mid/post-trained models and the communication rounds, separately, although they jointly contribute t…
GTFLAT, as a game theory-based add-on, addresses an important research question: How can a federated learning algorithm achieve better performance and training efficiency by setting more effective adaptive weights for averaging in the model aggregation phase? The main objectives for the ideal method of answering the question are: (1) empowering federated learning algorithms to reach better performance in fewer communication rounds, notably in the face of heterogeneous scenarios, and last but not least, (2) being easy to use alongside the state-of-the-art federated learning algorithms as a new …
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management
School of Computer Science and Technology, Algoma University, Canada（计算机科学与技术学院，阿尔戈马大学，加拿大）；Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic（电气工程系，布拉格捷克技术大学，捷克共和国）
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users …
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Zhongguancun Laboratory（中关村实验室）；Sichuan University（四川大学）；University at Buffalo（纽约州立大学布法罗分校）
The Hong Kong University of Science and Technology（香港科技大学）
Federated learning is vulnerable to various attacks, such as model poisoning and backdoor attacks, even if some existing defense strategies are used. To address this challenge, we propose an attack-adaptive aggregation strategy to defend against various attacks for robust federated learning. The proposed approach is based on training a neural network with an attention mechanism that learns the vulnerability of federated learning models from a set of plausible attacks. To the best of our knowledge, our aggregation strategy is the first one that can be adapted to defend against various attacks i…
Royal Institute of Technology (KTH)（皇家理工学院（KTH））；ABB Corporate Research Sweden（瑞典ABB企业研究中心）
Driven by the ever-increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth-generation (6G) mobile system enhanced by artificial intelligence (AI), has attracted substantial research interests. Among various candidate technologies of 6G, low earth orbit (LEO) satellites have appealing characteristics of ubiquitous wireless access. However, the costs of satellite communication (SatCom) are still high, relative to counterparts of ground mobile networks. To support massively interconnected devices with intelligent adaptive learnin…
Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL …
