The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport Systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized services tailored to their location information. However, this trajectory data contains sensitive information about users' movement patterns and habits, necessitating confidentiality and protection from unknown collectors. To address this challenge, privacy-preserving methods like K-anonymity and Differential Privacy have been proposed to safeguard private information in the dataset. Despite their effectiveness, these methods can impact the original features by introducing perturbations or generating unrealistic trajectory data, leading to suboptimal performance in downstream tasks. To overcome these limitations, we propose a Federated Variational AutoEncoder (FedVAE) approach, which effectively generates a new trajectory dataset while preserving the confidentiality of private information and retaining the structure of the original features. In addition, FedVAE leverages Variational AutoEncoder (VAE) to maintain the original feature space and generate new trajectory data, and incorporates Federated Learning (FL) during the training stage, ensuring that users' data remains locally stored to protect their personal information. The results demonstrate its superior performance compared to other existing methods, affirming FedVAE as a promising solution for enhancing data privacy and utility in location-based applications.
Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
School of Information Technology Deakin University Geelong, Australia（信息技术学院，德金大学，澳大利亚吉朗）；School of Information Technology Deakin University Geelong, Melbourne（信息技术学院，德金大学，澳大利亚吉朗墨尔本）；School of Information Technology, Deakin University, Geelong, Australia（信息技术学院，德金大学，澳大利亚吉朗）
Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness
ARTORG Center for Biomedical Engineering Research University of Bern（伯尔尼大学生物医学工程研究中心）；Graduate School for Cellular and Biomedical Sciences University of Bern（伯尔尼大学细胞与生物医学科学研究生院）
Federated Learning (FL) solves many of this decade's concerns regarding data privacy and computation challenges. FL ensures no data leaves its source as the model is trained at where the data resides. However, FL comes with its own set of challenges. The communication of model weight updates in this distributed environment comes with significant network bandwidth costs. In this context, we propose a mechanism of compressing the weight updates using Autoencoders (AE), which learn the data features of the weight updates and subsequently perform compression. The encoder is set up on each of the n…
Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices
Google Research, New York, NY（谷歌研究，纽约，纽约州）；University of California, Berkeley（加州大学伯克利分校）；University of Cambridge（剑桥大学）；University of Toronto（多伦多大学）；University of Melbourne（墨尔本大学）；University of Sydney（悉尼大学）
Deloitte & Touche LLP（德勤华永会计师事务所（特殊普通合伙））；Persistent Systems Limited（Persistent系统有限公司）
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior. Additionally, federated learning has provided a way for a global model to be trained with multiple clients' data without requiring the client to directly share their data. This paper proposes a novel anomaly detector via federated learning to detect malicious network activity on a client's server. In our experiments, we use an autoencoder with a classifier in a federated learning framework to determine if the network activity is ben…
Training deep learning models on in-home IoT sensory data is commonly used to recognise human activities. Recently, federated learning systems that use edge devices as clients to support local human activity recognition have emerged as a new paradigm to combine local (individual-level) and global (group-level) models. This approach provides better scalability and generalisability and also offers better privacy compared with the traditional centralised analysis and learning models. The assumption behind federated learning, however, relies on supervised learning on clients. This requires a large…
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder
CyLab Security and Privacy Institute, Carnegie Mellon University（卡内基梅隆大学CyLab安全与隐私研究所）；Real-Time and Embedded Computing Systems Research Centre (CISTER)（实时与嵌入式计算系统研究中心（CISTER））；Commonwealth Scientific and Industrial Research Organization (CSIRO)（英联邦科学与工业研究组织（CSIRO））；University of Cambridge（剑桥大学）；Alan Turing Institute（阿兰·图灵研究所）
Federated Learning is a new machine learning paradigm dealing with distributed model learning on independent devices. One of the many advantages of federated learning is that training data stay on devices (such as smartphones), and only learned models are shared with a centralized server. In the case of supervised learning, labeling is entrusted to the clients. However, acquiring such labels can be prohibitively expensive and error-prone for many tasks, such as human activity recognition. Hence, a wealth of data remains unlabelled and unexploited. Most existing federated learning approaches th…
MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks
School of Electrical and Computer Engineering（电气与计算机工程学院）；Max Planck Institute for Brain Research（马克斯·普朗克脑研究所在法兰克福）；School of Computer Engineering,University of Science and Technology (IUST)（科学技术大学（IUST）计算机工程学院）；Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Waterloo（滑铁卢大学）
Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks. Several federated learning frameworks employ differential privacy to prevent private data leakage to unauthorized parties and malicious attackers. Many studies, however, highlight the vulnerabilities of standard federated learning to poisoning and inferen…
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
Southern Illinois University（南伊利诺伊大学）；University of Asia Pacific（亚太大学）；Green University of Bangladesh（孟加拉国绿色大学）；University of North Texas（北得克萨斯大学）；John Hopkins University（约翰霍普金斯大学）
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；RPTU Kaiserslautern-Landau（凯撒斯劳滕-兰道工业大学）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）
Sun Yat-sen University（中山大学）；Arizona State University（亚利桑那州立大学）
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global…
University of Lille（里尔大学）；ENSAIT（国立高等艺术与纺织工业学院）；GEMTEX–Laboratoire de Génie et Matériaux Textiles（纺织工程与材料实验室）；University of Kent（肯特大学）
Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable a…
Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
Dept. of Information Studies, Trine University（信息研究系，特林大学）；Dept. of Business Information Systems, Central Michigan University（商业信息系统系，中央密歇根大学）；Dept. of CS, Maharishi International University（计算机科学系， Maharishi 国际大学）
Leverage Variational Graph Representation For Model Poisoning on Federated Learning
University of Cambridge（剑桥大学）；Real-Time and Embedded Computing Systems Research Centre (CISTER)（实时与嵌入式计算系统研究中心（CISTER））；Carnegie Mellon University（卡内基梅隆大学）；Commonwealth Scientific and Industrial Research Organization (CSIRO)（联邦科学与工业研究组织（CSIRO））；TU Berlin（柏林工业大学）；The University of Sydney（悉尼大学）
Nanyang Technological University (NTU)（南洋理工大学）；University of Maryland（马里兰大学）；China-Singapore International Joint Research Institute（中新国际联合研究院）
Object detection with on-board sensors (e.g., lidar, radar, and camera) play a crucial role in autonomous driving (AD), and these sensors complement each other in modalities. While crowdsensing may potentially exploit these sensors (of huge quantity) to derive more comprehensive knowledge, \textit{federated learning} (FL) appears to be the necessary tool to reach this potential: it enables autonomous vehicles (AVs) to train machine learning models without explicitly sharing raw sensory data. However, the multimodal sensors introduce various data heterogeneity across distributed AVs (e.g., labe…
Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH)（计算机科学研究所，研究与技术基金会-希腊）；Computer Science Department (CSD), University of Crete (UoC)（克里特大学计算机科学系）
Learning from Demonstration (LfD) has been established as the dominant paradigm for efficiently transferring skills from human teachers to robots. In this context, the Federated Learning (FL) conceptualization has very recently been introduced for developing large-scale human-robot collaborative environments, targeting to robustly address, among others, the critical challenges of multi-agent learning and long-term autonomy. In the current work, the latter scheme is further extended and enhanced, by designing and integrating a novel user profile formulation for providing a fine-grained represen…
Recent advances in wearable devices and Internet-of-Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging. In addition, data generated by different users bear various personal attributes and edge heterogeneity, rendering it impractical to develop a global model that adapts well to all users. Concerns over data privacy and communication costs also prohibit centralized data accumulation and training. We propose SemiPFL that supports edge users having no label or limited labeled datasets a…
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；State Key Laboratory of Integrated Services Networks (Xidian University)（西安电子科技大学综合业务网理论及关键技术国家重点实验室）；Qualcomm（高通公司）；School of Engineering, University of Kent（肯特大学工程学院）；School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）
PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT
Information Security Lab（信息安全部实验室）；University of Information Technology（信息技术大学）；Vietnam National University（越南国家大学）；VNU-HCM Information Security Center（VNU-HCM信息安全部中心）
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection
