FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
WeBank（微众银行）；Hong Kong University of Science and Technology（香港科技大学）；Extreme Vision Ltd（极视角有限公司）；Nanyang Technological University（南洋理工大学）；Joint NTU-WeBank Research Centre on FinTech（南大-微众金融科技联合研究中心）
Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, Qiang Yang
Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.
Queen Mary University of London（伦敦玛丽女王大学）；Assiut University（艾斯尤特大学）
Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this paradigm comes with various challenges. Specifically, in large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. Moreover, energy consumption across these battery-constrained devices is largely unexplored and a limitation for wide-adoption of FL. To address this issue, we develop …
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks
Institute of Information & Communications Technology Planning & Evaluation (IITP)-ITRC (Information Technology Research Center)（信息与通信技术规划与评估院（IITP）-ITRC（信息技术研究中心））
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室，计算机科学与工程学院，北京航空航天大学，北京，中国）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京，中国）；Center for AI Business Innovation, Department of Management Science and Systems, School of Management, University at Buffalo, USA（人工智能商业创新中心，管理科学与系统系，管理学院，布法罗大学，美国）
Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference
Department of Electrical Engineering, Indian Institute of Technology Madras（印度理工学院马德拉斯分校电子工程系）；Department of Computer Science and Engineering, Jadavpur University（贾达布尔大学计算机科学与工程系）
Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new federated learning framework which updates model from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT). SDAGFL has the advantage of personalization, resisting single point of failure and poisoning attack in fully decentralized federated learning. Because of these advantages, the SDAGFL is suitable for the federated learning in IoT scenario where the device is usually battery-powered. To promote the application of SDAGFL in IoT, we propose an energy optimized …
University of Calgary（卡尔加里大学）；Michigan State University（密歇根州立大学）
Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates. Low battery levels of clients eventually lead to their early dropouts from edge networks, loss of training data jeopardizing the performance of FL, and their availability to perform other desi…
We propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to train global models for portable devices such as smart phones. Federated learning is especially relevant for applications handling privacy-sensitive data, such as virtual keyboards, because training is performed without the users' data ever leaving their devices. While the principles of federated learning are fairly generic, its methodology assumes that the underlying models are neural networks. However, virtual keyboard…
University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；McGill University（麦吉尔大学）
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy. Yet, existing FL solutions usually rely on a centralized aggregator for model weight aggregation, while assuming clients are honest. Even if data privacy can still be preserved, the problem of single-point failure and data poisoning attack from malicious clients remains unresolved. To tackle this challenge, we propose to use distributed ledger technology (DLT) to achieve FLock, a secure and reliable decentralized Federated Learni…
University of Tokyo（东京大学）；United Nations University（联合国大学）
With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instead of traditional heuristics-based algorithms, more adaptive detection systems such as natural language processing (NLP)-powered approaches are essential to understanding phishing text representations. Nevertheless, concerns surrounding the collection of phishing data that might cover confidential information hinder the effectiveness of model learning. We propose a decentralized phishing email detection framework call…
Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL protocols has a critical communication bottleneck in a federated network coupled with privacy concerns, usually powered by a wide-area network (WAN). Such a WAN-driven FL design leads to significantly high cost and much slower model convergence. In this work, we propose an efficient FL protocol, which involves a hierarchical aggregation mechanism in the local-area network (LAN) due to its abundant bandwidth and almost n…
Data Poisoning and Leakage Analysis in Federated Learning
Georgia Institute of Technology（佐治亚理工学院）；Fordham University（福特汉姆大学）；National Institute of Standards and Technology（美国国家标准与技术研究院）；Georgia Tech Research Institute（佐治亚理工研究院）
Nanchang University（南昌大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Macau（澳门大学）
Federated learning struggles with their heavy energy footprint on battery-powered devices. The learning process keeps all devices awake while draining expensive battery power to train a shared model collaboratively, yet it may still leak sensitive personal information. Traditional energy management techniques in system kernel mode can force the training device entering low power states, but it may violate the SLO of the collaborative learning. To address the conflict between learning SLO and energy efficiency, we propose DEAL, an energy efficient learning system that saves energy and preserves…
Defending Against Poisoning Attacks in Federated Learning with Blockchain
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Imperial College London（帝国理工学院）；UiT The Arctic University of Norway（挪威北极大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；Avignon Université（阿维尼翁大学）
When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional server-grade hardware and are often battery powered, severely limiting the sophistication of models that can be trained under this paradigm. While most research has focused on designing better aggregation strategies to improve convergence rates and in alleviating the communication costs of …
Old Dominion University（欧道明大学）；Stony Brook University（纽约州立大学石溪分校）
Originated from distributed learning, federated learning enables privacy-preserved collaboration on a new abstracted level by sharing the model parameters only. While the current research mainly focuses on optimizing learning algorithms and minimizing communication overhead left by distributed learning, there is still a considerable gap when it comes to the real implementation on mobile devices. In this paper, we start with an empirical experiment to demonstrate computation heterogeneity is a more pronounced bottleneck than communication on the current generation of battery-powered mobile devi…
LLMs meet Federated Learning for Scalable and Secure IoT Management
School of Computer Science and Technology, Algoma University（计算机科学与技术学院，阿尔戈马大学）；School of Electrical Engineering and Computer Science, University of Ottawa（电气工程与计算机科学学院，渥太华大学）
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Electronics and Communication Engineering, the Shenzhen Campus of Sun Yat-sen University（中山大学电子与通信工程学院（深圳校区））；Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China（电子科技大学智能网络与通信中心）
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）
George Mason University（乔治梅森大学）；Old Dominion University（奥多明尼昂大学）
Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from the machine learning algorithms, the impact on the mobile system still remains unclear. In this paper, we design and implement an online optimization framework by connecting asynchronous execution of federated training with application co-running to minimize energy consumption on battery-powered mobile devices. From a series of experiments, we find that co-running the training process in the background with foreground …
As an edge intelligence algorithm for multi-device collaborative training, federated learning (FL) can reduce the communication burden but increase the computing load of wireless devices. In contrast, split learning (SL) can reduce the computing load of devices by using model splitting and assignment, but increase the communication burden to transmit intermediate results. In this paper, to exploit the advantages of FL and SL, we propose a hybrid federated split learning (HFSL) framework in wireless networks, which combines the multi-worker parallel update of FL and flexible splitting of SL. To…
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
MetaMind Innovation P.C.（MetaMind创新有限公司）；University of Western Macedonia（西马其顿大学）；K3Y Ltd（K3Y有限公司）；Democritus University of Thrace（色雷斯德谟克利特大学）；Kingston University London（伦敦金斯顿大学）
A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms
Cairo University（开罗大学）；Scientific Academy for Service Technology e.V. (ServTech)（服务技术科学协会（ServTech））；Egypt University of Informatics（埃及信息学大学）；University of New South Wales（新南威尔士大学）
University of Tartu（塔尔图大学）；Telefonica Research（西班牙电信研究院）
Machine Learning (ML)-powered apps are used in pervasive devices such as phones, tablets, smartwatches and IoT devices. Recent advances in collaborative, distributed ML such as Federated Learning (FL) attempt to solve privacy concerns of users and data owners, and thus used by tech industry leaders such as Google, Facebook and Apple. However, FL systems and models are still vulnerable to adversarial membership and attribute inferences and model poisoning attacks, especially in FL-as-a-Service ecosystems recently proposed, which can enable attackers to access multiple ML-powered apps. In this w…
Federated edge learning (FEEL) is a widely adopted framework for training an artificial intelligence (AI) model distributively at edge devices to leverage their data while preserving their data privacy. The execution of a power-hungry learning task at energy-constrained devices is a key challenge confronting the implementation of FEEL. To tackle the challenge, we propose the solution of powering devices using wireless power transfer (WPT). To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model…
