Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server. Although FL preserves local data privacy to some extent, it has been shown that information about clients' data can still be inferred from model updates. In recent years, various privacy-preserving schemes have been developed to address this privacy leakage. However, they often provide privacy at the expense of model performance or system efficiency, and balancing these tradeoffs is a crucial challenge when implementing FL schemes. In this manuscript, we propose a Privacy-Preserving Federated Learning (PPFL) framework built on the synergy of matrix encryption and system immersion tools from control theory. The idea is to immerse the learning algorithm, a Stochastic Gradient Decent (SGD), into a higher-dimensional system (the so-called target system) and design the dynamics of the target system so that: the trajectories of the original SGD are immersed/embedded in its trajectories, and it learns on encrypted data (here we use random matrix encryption). Matrix encryption is reformulated at the server as a random change of coordinates that maps original parameters to a higher-dimensional parameter space and enforces that the target SGD converges to an encrypted version of the original SGD optimal solution. The server decrypts the aggregated model using the left inverse of the immersion map. We show that our algorithm provides the same level of accuracy and convergence rate as the standard FL with a negligible computation cost while revealing no information about the clients' data.
Hong Kong University of Science and Technology（香港科技大学）；Clustar（星辰天合）；Peng Cheng Lab（鹏城实验室）
With the increasing awareness of privacy protection and data fragmentation problem, federated learning has been emerging as a new paradigm of machine learning. Federated learning tends to utilize various privacy preserving mechanisms to protect the transferred intermediate data, among which homomorphic encryption strikes a balance between security and ease of utilization. However, the complicated operations and large operands impose significant overhead on federated learning. Maintaining accuracy and security more efficiently has been a key problem of federated learning. In this work, we inves…
University of Stavanger（斯塔万格大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Old Dominion University（奥尔多明尼昂大学）；Norwegian University of Science and Technology（挪威科技大学）
Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applica…
More and more orgainizations and institutions make efforts on using external data to improve the performance of AI services. To address the data privacy and security concerns, federated learning has attracted increasing attention from both academia and industry to securely construct AI models across multiple isolated data providers. In this paper, we studied the efficiency problem of adapting widely used XGBoost model in real-world applications to vertical federated learning setting. State-of-the-art vertical federated XGBoost frameworks requires large number of encryption operations and ciphe…
Federated learning (FL) is a distributed machine learning paradigm that allows clients to collaboratively train a model over their own local data. FL promises the privacy of clients and its security can be strengthened by cryptographic methods such as additively homomorphic encryption (HE). However, the efficiency of FL could seriously suffer from the statistical heterogeneity in both the data distribution discrepancy among clients and the global distribution skewness. We mathematically demonstrate the cause of performance degradation in FL and examine the performance of FL over various datase…
Homomorphic encryption (HE) is a promising privacy-preserving technique for cross-silo federated learning (FL), where organizations perform collaborative model training on decentralized data. Despite the strong privacy guarantee, general HE schemes result in significant computation and communication overhead. Prior works employ batch encryption to address this problem, but it is still suboptimal in mitigating communication overhead and is incompatible with sparsification techniques. In this paper, we propose FLASHE, an HE scheme tailored for cross-silo FL. To capture the minimum requirements o…
In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the combined use of federated learning (FL) and encrypted images for privacy-preserving image classification under the use of the vision transformer (ViT). The proposed method allows us not only to train models over multiple participants without directly sharing their raw data but to also protect the privacy of test (query) images for the first time. In addition, it can also maintain the same accuracy as normally trained models. In an experiment, the proposed method was demonstr…
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location. This results in an improved generalizability of models and efficient scaling of computation as more sources and larger datasets are added to the federation. Nevertheless, recent membership attacks show that private or sensitive personal data can sometimes be leaked or inferred when model parameters or summary statistics are shared with a central site, requiring improved security solutions. In…
SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring
Institute of Industrial Automation and Software Engineering, University of Stuttgart（斯图加特大学工业自动化与软件工程研究所）；Eindhoven University of Technology (TU/e)（埃因霍温理工大学）
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
A. Korkmaz Dept. of Electrical Engineering \& Computer Science, The University of Missouri, Columbia, USA P. Rao Dept. of Electrical Engineering \& Computer Science, The University of Missouri, Columbia, USA
AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing
University of Southern California（南加州大学）；Islamic University of Technology（伊斯兰科技大学）；American International University-Bangladesh（孟加拉国美国国际大学）；Multimedia University（多媒体大学）
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
SVKM’s Dwarkadas J. Sanghvi College of Engineering（SVKM德瓦尔卡达斯·J·桑格维工程学院）；GSSS Institute of Engineering and Technology for Women（GSSS女子工程技术学院）；Purdue University（普渡大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；NYUAD Research Institute（纽约大学阿布扎比研究所）；University of Southern Denmark（南丹麦大学）；Tallinn University of Technology（塔林理工大学）；NASA Ames Research Center（NASA艾姆斯研究中心）；Universities Space Research Association（大学空间研究协会）
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
SVKM's Dwarkadas J. Sanghvi College of Engineering（SVKM的达沃拉德·J·桑格维工程学院）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（eBRAIN实验室，工程系，纽约大学阿布扎比分校（NYUAD））；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（量子与拓扑系统中心（CQTS），NYUAD研究机构）；The Maersk Mc-Kinney Moller Institute, University of Southern Denmark（马士克·麦克金尼·莫勒研究所，南丹麦大学）；Corvinus Institute for Advanced Studies (CIAS), Budapest, Hungary（科维努斯高级研究学院（CIAS），布达佩斯，匈牙利）；Davidson School of Chemical Engineering, Purdue University（戴维森化学工程学院，普渡大学）
As data privacy is gradually valued by people, federated learning(FL) has emerged because of its potential to protect data. FL uses homomorphic encryption and differential privacy encryption on the promise of ensuring data security to realize distributed machine learning by exchanging encrypted information between different data providers. However, there are still many problems in FL, such as the communication efficiency between the client and the server and the data is non-iid. In order to solve the two problems mentioned above, we propose a novel vertical federated learning framework based o…
An Efficient and Multi-private Key Secure Aggregation for Federated Learning
Southwest Jiaotong University（西南交通大学）；Canadian Institute of Cybersecurity（加拿大网络安全研究所）；University of New Brunswick（新不伦瑞克大学）；DBAPPSecurity Ltd（杭州迪普科技股份有限公司）
Australian National University（澳大利亚国立大学）；University of Sydney（悉尼大学）；University of Amsterdam（阿姆斯特丹大学）；N1Analytics；Data61
Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in enti…
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
University of Central Florida（佛罗里达中央大学）；Florida State University（佛罗里达州立大学）；Old Dominion University（旧 Dominion 大学）；University of Texas Health Science Center at Houston（德克萨斯大学健康科学中心休斯顿分校）
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua University（清华大学）
With the deployment of smart sensors and advancements in communication technologies, big data analytics have become vastly popular in the smart grid domain, informing stakeholders of the best power utilization strategy. However, these power-related data are stored and owned by different parties. For example, power consumption data are stored in numerous transformer stations across cities; mobility data of the population, which are important indicators of power consumption, are held by mobile companies. Direct data sharing might compromise party benefits, individual privacy and even national se…
iSING Lab, The Hong Kong University of Science and Technology（香港科技大学iSING实验室）；Clustar；Peng Cheng Lab（鹏城实验室）
In recent years, federated learning (FL) has been widely applied for supporting decentralized collaborative learning scenarios. Among existing FL models, federated logistic regression (FLR) is a widely used statistic model and has been used in various industries. To ensure data security and user privacy, FLR leverages homomorphic encryption (HE) to protect the exchanged data among different collaborative parties. However, HE introduces significant computational overhead (i.e., the cost of data encryption/decryption and calculation over encrypted data), which eventually becomes the performance …
University of Electronic Science and Technology of China（电子科技大学）；Macau University of Science and Technology（澳门科技大学）；Southwest Jiaotong University（西南交通大学）；China Telecommunications Corporation（中国电信集团有限公司）
Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models withou…
Technical University of Munich（慕尼黑工业大学）；Bytedance Inc.（字节跳动公司）；Massachusetts Institute of Technology（麻省理工学院）
Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boosting framework, to federated learning remains limited due to high cost incurred by conventional privacy-preserving methods. To address the problem, we propose two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP. Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication method to preserve privacy with lossless accuracy a…
A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the local data. Nevertheless, the existing federated learning frameworks have a serious defect that even a participant is revoked, its data are still remembered by the trained model. In a company-level cooperation, allowing the remaining companies to use a trained model that contains the memories from a revoked company is obviously unacceptable, because it can lead to a big conflict of interest. Therefore, we emphatical…
ConsenSys Health；Imperial College London（帝国理工学院）；UC San Diego（加州大学圣地亚哥分校）；Johns Hopkins Berman Institute of Bioethics（约翰斯·霍普金斯大学伯曼生物伦理研究所）
We propose a novel architecture for federated learning within healthcare consortia. At the heart of the solution is a unique integration of privacy preserving technologies, built upon native enterprise blockchain components available in the Ethereum ecosystem. We show how the specific characteristics and challenges of healthcare consortia informed our design choices, notably the conception of a new Secure Aggregation protocol assembled with a protected hardware component and an encryption toolkit native to Ethereum. Our architecture also brings in a privacy preserving audit trail that logs eve…
Hong Kong University of Science & Technology（香港科技大学）
In federated learning, multiple parties collaborate in order to train a global model over their respective datasets. Even though cryptographic primitives (e.g., homomorphic encryption) can help achieve data privacy in this setting, some partial information may still be leaked across parties if this is done non-judiciously. In this work, we study the federated learning framework of SecureBoost [Cheng et al., FL@IJCAI'19] as a specific such example, demonstrate a leakage-abuse attack based on its leakage profile, and experimentally evaluate the effectiveness of our attack. We then propose two se…
This paper introduces XFL, an industrial-grade federated learning project. XFL supports training AI models collaboratively on multiple devices, while utilizes homomorphic encryption, differential privacy, secure multi-party computation and other security technologies ensuring no leakage of data. XFL provides an abundant algorithms library, integrating a large number of pre-built, secure and outstanding federated learning algorithms, covering both the horizontally and vertically federated learning scenarios. Numerical experiments have shown the prominent performace of these algorithms. XFL buil…
Evaluating Selective Encryption Against Gradient Inversion Attacks
Information Networking Institute（信息网络研究所）；Carnegie Mellon University（卡内基梅隆大学）；TensorOpera AI；Department of Electrical and Computer Engineering（电气与计算机工程系）
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties. It is renowned for preserving privacy as the data never leaves the computational devices, and recent approaches further enhance its privacy by hiding messages transferred in encryption. However, we found that despite the efforts, federated learning remains privacy-threatening, due to its interactive nature across different parties. In this paper, we analyze the privacy threats in industrial-level federated learning frameworks with secure computation, and reveal such threats…
University of Pittsburgh（匹兹堡大学）；IBM Almaden Research Center（IBM阿尔马登研究中心）
Federated learning has emerged as a promising approach for collaborative and privacy-preserving learning. Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private. However, parameter interaction and the resulting model still might disclose information about the training data used. To address these privacy concerns, several approaches have been proposed based on differential privacy and secure multiparty computation (SMC), among others. They often result in large communicati…
Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data. However in most industries, data exists in the form of isolated islands and the data privacy and security is also an important issue. In this paper, we propose FedVGCN, a federated GCN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GCN models. Specifically, we split the computation graph data into two parts. For each iteration of the training process, the two parties transfer interme…
Data privacy has become an increasingly important issue in Machine Learning (ML), where many approaches have been developed to tackle this challenge, e.g. cryptography (Homomorphic Encryption (HE), Differential Privacy (DP), etc.) and collaborative training (Secure Multi-Party Computation (MPC), Distributed Learning and Federated Learning (FL)). These techniques have a particular focus on data encryption or secure local computation. They transfer the intermediate information to the third party to compute the final result. Gradient exchanging is commonly considered to be a secure way of trainin…
Johannes Gutenberg University Mainz（约翰内斯·古腾堡-美因茨大学）
Deep learning (DL) approaches are achieving extraordinary results in a wide range of domains, but often require a massive collection of private data. Hence, methods for training neural networks on the joint data of different data owners, that keep each party's input confidential, are called for. We address a specific setting in federated learning, namely that of deep learning from horizontally distributed data with a limited number of parties, where their vulnerable intermediate results have to be processed in a privacy-preserving manner. This setting can be found in medical and healthcare as …
WeBank（微众银行）；ShanghaiTech University（上海科技大学）；Hong Kong University of Science and Technology（香港科技大学）
Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers locally and exchanging encrypted information. This paper studies the vertical federated learning structure for logistic regression where the data sets at two parties have the same sample IDs but own disjoint subsets of features. Existing frameworks adopt the first-order stochastic gradient descent algorithm, which requires large number of communication rounds. To address the communication challenge, we propose a quasi…
Federated Learning (FL) is an approach to collaboratively train a model across multiple parties without sharing data between parties or an aggregator. It is used both in the consumer domain to protect personal data as well as in enterprise settings, where dealing with data domicile regulation and the pragmatics of data silos are the main drivers. While gradient boosted tree implementations such as XGBoost have been very successful for many use cases, its federated learning adaptations tend to be very slow due to using cryptographic and privacy methods and have not experienced widespread use. W…
University of Warwick（华威大学）；Meta AI；University of Virginia（弗吉尼亚大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically achieve the best results in a centralized non-secure setting, different models can excel when privacy and communication constraints are imposed. Instead, tree-based approaches such as XGBoost have attracted much attention for their high performance and ease of use; in particular, they often achieve state-of-the-art results on tabular data. Consequently, several recent works have focused on translating Gradient Boost…
University of Electronic Science and Technology of China（电子科技大学）；Peng Cheng Lab（鹏城实验室）；Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；The Chinese University of Hong Kong（香港中文大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Federated learning enables isolated clients to train a shared model collaboratively by aggregating the locally-computed gradient updates. However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server. Although the additive homomorphic encryption technique guarantees the security of this process, it brings unacceptable computation and communication burdens to FL participants. To mitigate this cost of secure aggregation and maintain the learning performance, we propose a new framework called Encoded Gradient Aggregation…
