Federated learning (FL) is a recently proposed distributed machine learning paradigm dealing with distributed and private data sets. Based on the data partition pattern, FL is often categorized into horizontal, vertical, and hybrid settings. Despite the fact that many works have been developed for the first two approaches, the hybrid FL setting (which deals with partially overlapped feature space and sample space) remains less explored, though this setting is extremely important in practice. In this paper, we first set up a new model-matching-based problem formulation for hybrid FL, then propose an efficient algorithm that can collaboratively train the global and local models to deal with full and partial featured data. We conduct numerical experiments on the multi-view ModelNet40 data set to validate the performance of the proposed algorithm. To the best of our knowledge, this is the first formulation and algorithm developed for the hybrid FL.
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning
University of Cincinnati（辛辛那提大学）；University of Wisconsin Milwaukee（威斯康星大学密尔沃基分校）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；Northeastern University（东北大学）
Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Models
1 School of Artificial Intelligence；Computer Science, Jiangnan University 2 Antai College of Economics \& Management, Shanghai Jiao Tong University 3 School of Computer Science, Shanghai Jiao Tong University 4 Cloud Computing；Distributed Systems Laboratory, University of Melbourne
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…
HybridFL: A Federated Learning Approach for Financial Crime Detection
Department of Advanced Computing Sciences, Maastricht University（马斯特里赫特大学高级计算科学系）；Department of Cognitive Science and Artificial Intelligence, Tilburg University（蒂尔堡大学认知科学与人工智能系）
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…
Technical University of Munich（慕尼黑工业大学）；University of Hamburg（汉堡大学）；University of Southern Denmark（南丹麦大学）；Imperial College London（帝国理工学院）
Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data sharing is not required, recent studies have shown that FL might leak the private data of a client through the model parameters shared with the server or the other clients. In this paper, we present the HyFed framework, which enhances the privacy of FL while preserving the utility of the global model. HyFed provides developers with a generic API to develop federated, privacy-preserving algorithms. HyFed supports bot…
Department of Engineering Sciences and Applied Mathematics, Northwestern University（工程科学与应用数学系，西北大学）；Department of Industrial Engineering and Management Sciences, Northwestern University（工业工程与管理科学系，西北大学）
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
School of Communications and Electronics Engineering, East China Normal University（通信与电子工程学院，东华大学）；School of Electronics and Information Technology, Sun Yat-sen University（电子与信息学院，中山大学）；Zhejiang University/University of Illinois at Urbana-Champaign Institute, Zhejiang University（浙江大学/伊利诺伊大学厄巴纳-香槟分校研究院，浙江大学）；Department of Electronic Engineering, State Key Laboratory of Space Network and Communications, Tsinghua University（电子工程系，空间网络与通信国家重点实验室，清华大学）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（信息系统技术与设计学院，新加坡科技设计大学）
Machine learning for hybrid beamforming has been extensively studied by using centralized machine learning (CML) techniques, which require the training of a global model with a large dataset collected from the users. However, the transmission of the whole dataset between the users and the base station (BS) is computationally prohibitive due to limited communication bandwidth and privacy concerns. In this work, we introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the BS by collecting only the gradients from the users. We design …
Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By executing FL, a set of agents can jointly train a model without sharing their datasets with each other, or a third-party. This makes FL particularly suitable for settings where data privacy is desired. At the same time, concealing training data gives attackers an opportunity to inject backdoors into the trained model. It has been shown that an attacker can inject backdoors to the trained model during FL, and then can leverage the backdoor to make the model misclassify later. Several works tried to allevia…
Koc University（科克大学）；United States CCDC Army Research Laboratory（美国CCDC陆军研究实验室）
Many of the machine learning (ML) tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning (FL) overcomes this issue by allowing the clients to send only the model updates to the PS instead of the whole dataset. In this way, FL brings the learning to edge level, wherein powerful computational resources are required on the client side. This requirement may not always be satisfied because of diverse computational capabilities of edge devices. We addres…
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT
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））；Commonwealth Scientific and Industrial Research Organization (CSIRO)（英联邦科学与工业研究组织（CSIRO））；University of New South Wales（新南威尔士大学）；Nanyang Technological University（南洋理工大学）；Koç University（科克大学）
Northwestern Polytechnical University（西北工业大学）；Alibaba Group（阿里巴巴集团）；Monash University（莫纳什大学）；The Hong Kong Polytechnic University（香港理工大学）
Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model updates aggregated from the randomly-selected unrepresentative subsets directly slows the FL convergence. We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction. Simple yet effective schemes are designed to improve the clustering effect and control the effect fluctuation, therefore, generating the client subset with certain representativeness of sampling. Theo…
Korea Advanced Institute of Science and Technology（韩国科学技术院）；King’s College London（伦敦国王学院）
Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenges arising from the presence of wireless communication links. We specifically consider wireless implementations of Federated Learning (FL) and Federated Distillation (FD), as well as of a novel Hybrid Federated Distillation (HFD) scheme. Both digital implementations based on separate source-channel coding and over-the-air computing implementations based on joint source-channel coding are proposed and evaluated over Ga…
Critical role of Internet of Things (IoT) in various domains like smart city, healthcare, supply chain and transportation has made them the target of malicious attacks. Past works in this area focused on centralized Intrusion Detection System (IDS), assuming the existence of a central entity to perform data analysis and identify threats. However, such IDS may not always be feasible, mainly due to spread of data across multiple sources and gathering at central node can be costly. Also, the earlier works primarily focused on improving True Positive Rate (TPR) and ignored the False Positive Rate …
Georgia Institute of Technology（佐治亚理工学院）；IBM Research Almaden（IBM阿尔马登研究中心）
Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or …
Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院清华-伯克利深圳学院）；Tsinghua University（清华大学）
In a modern power system, real-time data on power generation/consumption and its relevant features are stored in various distributed parties, including household meters, transformer stations and external organizations. To fully exploit the underlying patterns of these distributed data for accurate power prediction, federated learning is needed as a collaborative but privacy-preserving training scheme. However, current federated learning frameworks are polarized towards addressing either the horizontal or vertical separation of data, and tend to overlook the case where both are present. Further…
Manipal Institute of Technology（马尼帕尔理工学院）；Carnegie Mellon University（卡内基梅隆大学）
The amount of data, manpower and capital required to understand, evaluate and agree on a group of symptoms for the elementary prognosis of pandemic diseases is enormous. In this paper, we present FedPandemic, a novel noise implementation algorithm integrated with cross-device Federated learning for Elementary symptom prognosis during a pandemic, taking COVID-19 as a case study. Our results display consistency and enhance robustness in recovering the common symptoms displayed by the disease, paving a faster and cheaper path towards symptom retrieval while also preserving the privacy of patient'…
Purdue University（普渡大学）；Arizona State University（亚利桑那州立大学）；NC State University（北卡罗来纳州立大学）
Federated learning has generated significant interest, with nearly all works focused on a "star" topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through the network dimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra- and inter-layer model learning that considers the network as a multi-layer cluster-based structure. MH-FL considers the topology structures among the nodes in the clus…
University of Warwick（华威大学）；South China University of Technology（华南理工大学）；Shenzhen University（深圳大学）
Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality X…
Duzce University（杜兹大学）；University of Luxembourg（卢森堡大学）；Koc University（科奇大学）；University of Hertfordshire（赫特福德大学）
Many of the machine learning tasks rely on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) entailing huge communication overhead. To overcome this, federated learning (FL) has been suggested as a promising tool, wherein the clients send only the model updates to the PS instead of the whole dataset. However, FL demands powerful computational resources from the clients. In practice, not all the clients have sufficient computational resources to participate in training. To address this common scenario, we propose a more effi…
Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；Department of Computer Science, Bahria University（巴里亚大学计算机科学系）；School of Mechanical Engineering, Southwest Jiaotong University（西南交通大学机械工程学院）
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China（浙江工业大学信息工程学院，杭州 310023，中国）；Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems, Hangzhou, China（浙江省复杂系统智能感知与控制重点实验室，杭州，中国）；College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China（浙江大学控制科学与工程学院，杭州 310027，中国）；Computing Science and Internet of Things, Newcastle University, NE1 7RU Newcastle, UK（新castle大学计算科学与物联网学院，NE1 7RU Newcastle, UK）
We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w.r.t. training time, accuracy, etc., comparing to idealized non-private VFL. Our work builds on the recent advances in VFL-based collaborative training among different organizations which rely on protocols like Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC) to secure computation and training. In particular, we analy…
This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available benchmarks for federated learning have focused mainly on synthetic datasets and use a limited number of applications. OARF mimics more realistic application scenarios with publicly available data sets as different data silos in image, text and structured data. Our characterization shows that the benchmark suite is diverse in data size, distribution, feature distribution and learning task complexity. The extensive eval…
Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggr…
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）
United States CCDC Army Research Laboratory（美国CCDC陆军研究实验室）
Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on …
