In the evolving landscape of Federated Learning (FL), the challenge of ensuring data integrity against poisoning attacks is paramount, particularly for applications demanding stringent privacy preservation. Traditional anomaly detection strategies often struggle to adapt to the distributed nature of FL, leaving a gap our research aims to bridge. We introduce a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision. Our approach uniquely combines detecting anomalous gradient patterns with identifying reconstruction errors, significantly enhancing FL model security. Validated through extensive experiments on MNIST and CIFAR-10 datasets, our method outperforms existing solutions by 15\% in anomaly detection accuracy while maintaining a minimal false positive rate. This robust performance, consistent across varied data types and network sizes, underscores our framework's potential in securing FL deployments in critical domains such as healthcare and finance. By setting new benchmarks for anomaly detection within FL, our work paves the way for future advancements in distributed learning security.
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…
Internet of Medical Things (IoMT) is becoming ubiquitous with a proliferation of smart medical devices and applications used in smart hospitals, smart-home based care, and nursing homes. It utilizes smart medical devices and cloud computing services along with core Internet of Things (IoT) technologies to sense patients' vital body parameters, monitor health conditions and generate multivariate data to support just-in-time health services. Mostly, this large amount of data is analyzed in centralized servers. Anomaly Detection (AD) in a centralized healthcare ecosystem is often plagued by signi…
IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DÏoT, an autonomous s…
Bruno Kessler Foundation（布鲁诺·凯斯勒基金会）；IBM Research（IBM研究院）
Federated Leaning is an emerging approach to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent's performance without disclosing any data. In this paper we present a novel algorithmic architecture that tackle this problem in the particular case of Anomaly Detection (or classification or rare events), a setting where typical applications often comprise data with sensible information, but where the scarcity of anomalous examples encourages collaboration. We show how Random Forests can be used as a tool for the developmen…
City University of Hong Kong（香港城市大学）；University of Miami（迈阿密大学）；Sun Yat-sen University（中山大学）；Dalian University of Technology（大连理工大学）
Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the privacy issues in distributed learning, where only learning models will be transmitted from the distributed nodes to servers without revealing users' own data and hence protecting the privacy of users. In this paper, we propose a horizontal federated XGBoost algorithm to solve the fe…
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）；School of Data Science, The Chinese University of HongKong (Shenzhen)（香港中文大学（深圳）数据科学学院）；School of Computer Science, Northwest Polytechnical University（西北工业大学计算机学院）；College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）
Inserting a backdoor into the joint model in federated learning (FL) is a recent threat raising concerns. Existing studies mostly focus on developing effective countermeasures against this threat, assuming that backdoored local models, if any, somehow reveal themselves by anomalies in their gradients. However, this assumption needs to be elaborated by identifying specifically which gradients are more likely to indicate an anomaly to what extent under which conditions. This is an important issue given that neural network models usually have huge parametric space and consist of a large number of…
Over the last two decades, a lot of work has been done in improving network security, particularly in intrusion detection systems (IDS) and anomaly detection. Machine learning solutions have also been employed in IDSs to detect known and plausible attacks in incoming traffic. Parameters such as packet contents, sender IP and sender port, connection duration, etc. have been previously used to train these machine learning models to learn to differentiate genuine traffic from malicious ones. Generative Adversarial Networks (GANs) have been significantly successful in detecting such anomalies, mos…
Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intellig…
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
University of California, Irvine（加州大学尔湾分校）；Texas A&M University（德克萨斯农工大学）；University of Birmingham（伯明翰大学）；University of Southern California（南加州大学）；Palo Alto Networks（帕洛阿尔托网络）；Carnegie Mellon University（卡内基梅隆大学）
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach
Centre for Cyber Resilience and Trust (CREST), Deakin University（网络弹性与信任中心（CREST），迪肯大学）；Department of Computer Science & IT, Latrobe University（计算机科学与信息技术系，拉筹伯大学）
Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared gradient. It raises concerns about FL since input leakage contradicts the privacy-preserving intention of using FL. Despite a relatively rich literature on attacks and defenses of input reconstruction in Horizontal FL, input leakage and protection in vertical FL starts to draw researcher's attention recently. In this paper, we study how to defend against input leakage attacks in Vertical FL. We design an adversarial …
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）
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
Department of Computer, Control and Management Engineering, Sapienza University of Rome（计算机、控制与管理工程系，罗马萨皮恩扎大学）；National Research Council in Pisa（比萨国家研究理事会）；Department of Statistics, Ludwig-Maximilians-University of Munich（统计学系，慕尼黑路德维希-马克西米利安大学）；Department of Computer Science, Sapienza University of Rome（计算机科学系，罗马萨皮恩扎大学）
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
Honda Research Institute EU（本田欧洲研究所）；University of Kent（肯特大学）；University of Lille（里尔大学）；ENSAIT；Dong A University（东亚大学）；International Research Institute for Artificial Intelligence and Data Science（人工智能与数据科学国际研究院）
Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of participants to construct a joint ML model without exposing their private training data. Existing FL designs have been shown to exhibit vulnerabilities which can be exploited by adversaries both within and outside of the system to compromise data privacy. However, most current works conduct attacks by leveraging gradients on a small batch of data, which is less practical in FL. In this work, we consider a more practical and interesting scenario in which participants share their epoch-averaged gradients (s…
The idea of federated learning is to train deep neural network models collaboratively and share them with multiple participants without exposing their private training data to each other. This is highly attractive in the medical domain due to patients' privacy records. However, a recently proposed method called Deep Leakage from Gradients enables attackers to reconstruct data from shared gradients. This study shows how easy it is to reconstruct images for different data initialization schemes and distance measures. We show how data and model architecture influence the optimal choice of initial…
Approximate and Weighted Data Reconstruction Attack in Federated Learning
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University（南洋理工大学数学科学学院，物理与数学科学学院）；Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg（动态、控制、机器学习和数值学主席职位，数学系，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；Chair of Computational Mathematics, Fundación Deusto（计算数学主席，德乌斯基金会）；Departamento de Matemáticas, Universidad Autónoma de Madrid（数学系，马德里自治大学）
North Carolina State University（北卡罗来纳州立大学）；Zhejiang University（浙江大学）
Federated learning has been proposed as a privacy-preserving machine learning framework that enables multiple clients to collaborate without sharing raw data. However, client privacy protection is not guaranteed by design in this framework. Prior work has shown that the gradient sharing strategies in federated learning can be vulnerable to data reconstruction attacks. In practice, though, clients may not transmit raw gradients considering the high communication cost or due to privacy enhancement requirements. Empirical studies have demonstrated that gradient obfuscation, including intentional …
\ac{fl} proposed a distributed \ac{ml} framework where every distributed worker owns a complete copy of global model and their own data. The training is occurred locally, which assures no direct transmission of training data. However, the recent work \citep{zhu2019deep} demonstrated that input data from a neural network may be reconstructed only using knowledge of gradients of that network, which completely breached the promise of \ac{fl} and sabotaged the user privacy. In this work, we aim to further explore the theoretical limits of reconstruction, speedup and stabilize the reconstruction pr…
UIFV: Data Reconstruction Attack in Vertical Federated Learning
School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Shanghai Blockchain Engineering Research Center（上海区块链工程研究中心）；Institute of Financial Technology, Fudan University（复旦大学金融科技研究院）；School of Software, Nanjing University of Information Science and Technology（南京信息工程大学软件学院）
