State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；The Future Network of Intelligence Institute, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）未来智联网络研究院）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）
Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. It allows central servers to remove historical data effects within the machine learning model as well as address the "right to be forgotten" issue in federated learning. However, existing works require central servers to retain the historical model parameters from distributed clients, such that allows the central server to utilize these parameters for further training even, after the clients exit the training process. To address this issue, this paper proposes a new blockchain-enabled trustworthy federated unlearning framework. We first design a proof of federated unlearning protocol, which utilizes the Chameleon hash function to verify data removal and eliminate the data contributions stored in other clients' models. Then, an adaptive contribution-based retraining mechanism is developed to reduce the computational overhead and significantly improve the training efficiency. Extensive experiments demonstrate that the proposed framework can achieve a better data removal effect than the state-of-the-art frameworks, marking a significant stride towards trustworthy federated unlearning.
Zhejiang University（浙江大学）；Fudan University（复旦大学）；University of Manchester（曼彻斯特大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlea…
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we pre…
Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN)（里约格兰德北教育科学和技术联邦学院）
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing
Knight Foundation School of Computing and Information Sciences, Florida International University (FIU)（佛罗里达国际大学奈特基金会计算与信息科学学院）；School of Computing, Southern Illinois University（南伊利诺伊大学计算学院）；Enterprise and Logistics Engineering program at FIU（佛罗里达国际大学企业与物流工程项目）
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain
School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Chainbase Inc.（Chainbase公司）；PublicAI Inc.（PublicAI公司）
Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce trustworthy decisions. Upon the completion of collaborative training, an agent may decide to exercise her legal "right to be forgotten", which calls for her contribution to the jointly trained model to be deleted and discarded. This paper studies federated learning and unlearning in a decentralized network within a Bayesian framework. It specifically develops federated variational inference (VI) solutions based on the d…
Université de Sherbrooke（舍布鲁克大学）；GREYC CNRS（法国国家科学研究中心灰西实验室）；ENSICAEN（法国国立卡昂高等工程师学院）；Normandie University（诺曼底大学）；University of Technology of Troyes（特鲁瓦技术大学）
Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to F…
Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
The Hong Kong Polytechnic University（香港理工大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Santa Cruz（加州大学圣克ruz分校）；Huawei Technologies（华为技术有限公司）；Research Centre for Privacy and Security Technologies in Future Smart Systems, PolyU（未来智能系统中的隐私与安全技术研究中心，PolyU）
Rethinking Machine Unlearning for Large Language Models
Michigan State University（密歇根州立大学）；IBM Research（IBM研究院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；ByteDance Research（字节跳动研究院）；Stanford University（斯坦福大学）
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
University of Southern Queensland（南昆士兰大学）；Lingnan University（岭南大学）；Wuhan University of Technology（武汉理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Hong Kong Polytechnic University（香港理工大学）
本文系统综述机器遗忘的最新方法、指标、数据集与挑战，并指出其在隐私、安全、公平及可信 AI 中的未来方向。
Concordia University（康考迪亚大学）；University of Calgary（卡尔加里大学）
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled…
Université catholique de Louvain（天主教鲁汶大学）；Massachusetts Institute of Technology（麻省理工学院）
Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equitable sharing of the learned model among each member of the coalition by using adequate encryption and blockchain mechanisms. We exemplify its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；Department of Computing, Hong Kong Polytechnic University（香港理工大学计算机系）；Data61, CSIRO（数据61，CSIRO）；Department of Electronics Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）
Lero – the Irish Software Research Centre（Lero——爱尔兰软件研究中心）；National University of Ireland（爱尔兰国立大学）
The knowledge, embodied in machine learning models for intelligent systems, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labelling, network training, and fine-tuning of models. Sharing and reuse of these elaborated models between intelligent systems deployed in a different environment, which is known as transfer learning, would facilitate the adoption of services for the users and accelerates the uptake of intelligent systems in environments such as smart building and smart city applications. In this context, the communication and kn…
