Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the security provided by the protocols of blind quantum computation, the cooperation between quantum physics and machine learning may lead to unparalleled prospect for solving private distributed learning tasks. In this paper, we introduce a quantum protocol for distributed learning that is able to utilize the computational power of the remote quantum servers while keeping the private data safe. For concreteness, we first introduce a protocol for private single-party delegated training of variational quantum classifiers based on blind quantum computing and then extend this protocol to multiparty private distributed learning incorporated with differential privacy. We carry out extensive numerical simulations with different real-life datasets and encoding strategies to benchmark the effectiveness of our protocol. We find that our protocol is robust to experimental imperfections and is secure under the gradient attack after the incorporation of differential privacy. Our results show the potential for handling computationally expensive distributed learning tasks with privacy guarantees, thus providing a valuable guide for exploring quantum advantages from the security perspective in the field of machine learning with real-life applications.
Due to the rising concerns on privacy protection, how to build machine learning (ML) models over different data sources with security guarantees is gaining more popularity. Vertical federated learning (VFL) describes such a case where ML models are built upon the private data of different participated parties that own disjoint features for the same set of instances, which fits many real-world collaborative tasks. Nevertheless, we find that existing solutions for VFL either support limited kinds of input features or suffer from potential data leakage during the federated execution. To this end,…
Bilkent University（比尔肯特大学）；Huawei Turkey Research and Development Center (HTRDC)（华为土耳其研发中心）
We study collaborative machine learning systems where a massive dataset is distributed across independent workers which compute their local gradient estimates based on their own datasets. Workers send their estimates through a multipath fading multiple access channel with orthogonal frequency division multiplexing to mitigate the frequency selectivity of the channel. We assume that there is no channel state information (CSI) at the workers, and the parameter server (PS) employs multiple antennas to align the received signals. To reduce the power consumption and the hardware costs, we employ co…
Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregation
Department of Electrical and Computer Engineering, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校电气与计算机工程系）；Electrical Engineering Department, Faculty of Engineering, Alexandria University（亚历山大大学工程学院电子工程系）
Chalmers University of Technology（查尔姆斯理工大学）；Zhejiang University（浙江大学）；Research Institutes of Sweden（瑞典研究院）
Bandwidth slicing is introduced to support federated learning in edge computing to assure low communication delay for training traffic. Results reveal that bandwidth slicing significantly improves training efficiency while achieving good learning accuracy.
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）
Pennsylvania State University（宾夕法尼亚州立大学）；Fudan University（复旦大学）
Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each has unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. The…
HPC-AI Technology Inc.（HPC-AI技术公司）；National University of Singapore（新加坡国立大学）
Federated learning is proposed by Google to safeguard data privacy through training models locally on users' devices. However, with deep learning models growing in size to achieve better results, it becomes increasingly difficult to accommodate the whole model on one single device. Thus, model parallelism is then used to divide the model weights among several devices. With this logic, the approach currently used evenly allocates weights among devices. However, in reality, a computation bottleneck may occur resulting from variant computing power of different users' devices. To address this prob…
Mobile Edge Computing (MEC) has been a promising paradigm for communicating and edge processing of data on the move. We aim to employ Federated Learning (FL) and prominent features of blockchain into MEC architecture such as connected autonomous vehicles to enable complete decentralization, immutability, and rewarding mechanisms simultaneously. FL is advantageous for mobile devices with constrained connectivity since it requires model updates to be delivered to a central point instead of substantial amounts of data communication. For instance, FL in autonomous, connected vehicles can increase …
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Cyberspace Security Research Center, Peng Cheng Laboratory（鹏城实验室网络空间安全研究中心）；School of Electrical and Data Engineering, University of Technology Sydney（悉尼科技大学电气与数据工程学院）；Department of Computer Science and Technology, Fuzhou University（福州大学计算机科学与技术学院）；Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology（吕勒奥理工大学计算机、电气与空间工程系）
In recent years, mobile clients' computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising distributed machine learning solution that uses local computing and local data to train the Artificial Intelligence (AI) model. Combining local computing and federated learning can train a powerful AI model under the premise of ensuring local data privacy while making full use of mobile clients' resources. However, the heterogeneity of local data, that is, Non-independent and identical distribution (Non-IID) and imb…
Owing to the large volume of sensed data from the enormous number of IoT devices in operation today, centralized machine learning algorithms operating on such data incur an unbearable training time, and thus cannot satisfy the requirements of delay-sensitive inference applications. By provisioning computing resources at the network edge, Mobile Edge Computing (MEC) has become a promising technology capable of collaborating with distributed IoT devices to facilitate federated learning, and thus realize real-time training. However, considering the large volume of sensed data and the limited reso…
Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing
Division of Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway（挪威癌症登记处，挪威公共卫生研究所，奥斯陆）；Department of Physics and Technology, The Arctic University of Norway, Tromsø, Norway（挪威极地大学物理与技术系）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Macquarie University（麦考瑞大学）
Pervasive computing applications commonly involve user's personal smartphones collecting data to influence application behavior. Applications are often backed by models that learn from the user's experiences to provide personalized and responsive behavior. While models are often pre-trained on massive datasets, federated learning has gained attention for its ability to train globally shared models on users' private data without requiring the users to share their data directly. However, federated learning requires devices to collaborate via a central server, under the assumption that all users …
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Argonne National Laboratory（阿贡国家实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；VMWare Research（威睿研究院）；The University of Chicago（芝加哥大学）
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parking space estimation without exchanging the raw data. Furthermore, we investigate the management of Parked Vehicle assisted Edge Computing (PVEC) by FedParking. In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services throug…
Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. It can provide a general decentralized SGD analytical framework. We establish strong convergence guarantees for the proposed DFL algorithm without the assumpt…
Florida International University（佛罗里达国际大学）；Southern Illinois University（南伊利诺伊大学）
Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of F…
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…
Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous works have shown that non-Independent and Identically Distributed (non-IID) user data harms the convergence speed of the FL algorithms. Furthermore, most existing work on FL measures global-model accuracy, but in many cases, such as user content-recommendation, improving individual User model Accuracy (UA) is the real objective. To address these issues, we propose a Multi-Task FL (MTFL) algorithm that introduces non-…
Baidu Inc.（百度公司）；Auburn University（奥本大学）；University of Oregon（俄勒冈大学）
In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distributed data and computing resources cannot be directly shared among different regions or organizations for machine learning tasks. Federated learning emerges as an efficient approach to exploit distributed data and computing resources, so as to collaboratively train machine learning models, while obeying the laws and regulations and ensuring data security and data privacy. In this paper, we provide a comprehensive survey…
Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recently: an advanced use of edge resources and the integration of machine learning techniques for engineering applications. This evolution raises major challenges, in particular related to the appropriate distribution of computing elements along an edge-to-cloud continuum. About this, Federated Learning has been recently proposed for distributed model training in the edge. The principle of this approach is to aggregate mo…
University of Southern California（南加州大学）；Intel Corporation（英特尔公司）；Broadcom Corporation（博通公司）；Intel Labs（英特尔实验室）；Qualcomm（高通公司）
Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. CodedFedL enables coded co…
Institute for Language and Speech Processing, Athena Research Center（雅典娜研究中心语言与语音处理研究所）；Department of Electrical and Computer Engineering, Democritus University of Thrace（色雷斯德谟克利特大学电气与计算机工程系）
With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs). Traditional systems consider a centralized approach that requires the transmission and collection of users' private data. In this work, we present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-$N$ POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner's device, while the local updates…
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
School of EIEE, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；IoT Thrust, the HKUST (GZ)（香港科技大学（广州）物联网枢纽）；Department of ECE, the HKUST（香港科技大学电子与计算机工程学系）
Singapore University of Design and Technology（新加坡科技设计大学）；East China Normal University（华东师范大学）；Zhejiang University（浙江大学）
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UE…
University of Southern California（南加州大学）；Intel Labs（英特尔实验室）
Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralized server. Performance of FL in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. A recent work, Coded Federated Learning (CFL), proposes to mitigate stragglers and speed up training for linear regression tasks by assigning redundant computations at the MEC server. Coding redundanc…
With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated l…
University of Houston（休斯顿大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Purple Mountain Laboratories（紫金山实验室）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communi…
Cluster-Based Client Selection for Dependent Multi-Task Federated Learning in Edge Computing
Department of Statistics, University of Oxford（牛津大学统计系）；Guangdong Prov./Zhuhai Key Lab of IRADS, Beijing Normal-Hong Kong Baptist University（广东/珠海IRADS重点实验室，北京师范大学-香港 Baptist大学）；Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University（精准医学研究院，中山大学第一附属医院）
Federated learning is a prime candidate for distributed machine learning at the network edge due to the low communication complexity and privacy protection among other attractive properties. However, existing algorithms face issues with slow convergence and/or robustness of performance due to the considerable heterogeneity of data distribution, computation and communication capability at the edge. In this work, we tackle both of these issues by focusing on the key component of model aggregation in federated learning systems and studying optimal algorithms to perform this task. Particularly, we…
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
Southwestern University of Finance and Economics（西南财经大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Southwest Jiaotong University（西南交通大学）
Sun Yat-sen University（中山大学）；WeBank Co. Ltd.（微众银行股份有限公司）
Federated learning has been widely studied and applied to various scenarios. In mobile computing scenarios, federated learning protects users from exposing their private data, while cooperatively training the global model for a variety of real-world applications. However, the security of federated learning is increasingly being questioned, due to the malicious clients or central servers' constant attack to the global model or user privacy data. To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i.e., a Blockchain-based Federated Lear…
Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on distributed clients to obtain a more general model. In this way, no private data is sent over the network, and the communication cost is reduced. However, current solutions rely on the availability of large amounts of stored data at the client side in order to fine-tune the models sent by the server. Such setting is not realistic in mobile pervasive computing where data storage must be kept low and data characteristic…
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Department of Computer Science and Engineering, BITS Pilani Dubai Campus（计算机科学与工程系，比斯帕尼迪拜校区）；Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科技大学）
