Federated Learning for IoT Devices With Domain Generalization

Article Computer Science, Information Systems

A Survey on Federated Learning for Resource-Constrained IoT Devices

Ahmed Imteaj et al.

Summary: This article investigates the methods and challenges of training distributed machine learning models in resource-constrained IoT environments, discusses the limitations of existing research, and identifies future research directions.

IEEE INTERNET OF THINGS JOURNAL (2022)

添加到收藏夹

Article Computer Science, Information Systems

A new intrusion detection method for cyber-physical system in emerging industrial IoT

Himanshu Mittal et al.

Summary: This paper presents a new clustering method for intrusion detection in the Industrial Internet-of-Things, and validates its efficacy through experimental analysis on benchmark functions and Industrial Internet-of-Things datasets. The proposed method outperforms existing methods in terms of F-measure and computation time, ensuring security in a real-time Industrial Internet-of-Things environment.

COMPUTER COMMUNICATIONS (2022)

添加到收藏夹

Article Computer Science, Information Systems

Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT-Edge Devices

Segun Popoola et al.

Summary: This article proposes a federated deep learning method for zero-day botnet attack detection in IoT-edge devices. The method generates a global model by coordinating the training of independent models in multiple IoT-edge devices, achieving high-performance classification detection of zero-day botnet attacks and ensuring data privacy and security.

IEEE INTERNET OF THINGS JOURNAL (2022)

添加到收藏夹

Article Computer Science, Information Systems

A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

Ruijie Zhao et al.

Summary: This article proposes a lightweight deep neural network (LNN) based NID method for IoT, which achieves excellent classification performance and is suitable for classifying IoT traffic through feature dimensionality reduction and low computational cost feature extraction.

IEEE INTERNET OF THINGS JOURNAL (2022)

添加到收藏夹

Article Computer Science, Information Systems

semi-Traj2Graph Identifying Fine-Grained Driving Style With GPS Trajectory Data via Multi-Task Learning

Chao Chen et al.

Summary: This article proposes a multi-task learning framework called semi-Traj2Graph to accurately identify fine-grained driving styles in the temporal dimension. The framework combines graph representation learning and semi-supervised learning methods, achieving high accuracy on taxi trajectory datasets from two cities in China.

IEEE TRANSACTIONS ON BIG DATA (2022)

添加到收藏夹

Article Computer Science, Information Systems

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Yang Zhao et al.

Summary: This study introduces a federated learning system leveraging a reputation mechanism to assist home appliance manufacturers in developing a smart home system. The system involves two stages: centralized training of customer data and model sharing among manufacturers through a reputation-based mechanism.

IEEE INTERNET OF THINGS JOURNAL (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation

Guile Wu et al.

Summary: In this study, a new approach called COPA is proposed to optimize a generalized target model for decentralized domain generalization and multi-source unsupervised domain adaptation. COPA achieves comparable performance against state-of-the-art methods without the need for centralized data collection by optimizing local models and centrally aggregating feature extractors and classifiers.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Quande Liu et al.

Summary: This paper addresses the problem of federated domain generalization and proposes a novel approach, ELCFS, which enables each client to exploit multi-source data distributions through interpolation in continuous frequency space. A boundary-oriented episodic learning paradigm is carefully designed to improve model generalization performance in medical image segmentation tasks. The effectiveness of the method is demonstrated through superior performance over state-of-the-arts on two medical image segmentation tasks.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

添加到收藏夹

Article Computer Science, Information Systems

Federated Learning for Internet of Things: A Comprehensive Survey

Dinh C. Nguyen et al.

Summary: The Internet of Things and artificial intelligence are increasingly intertwined in our daily lives, with Federated Learning emerging as a distributed collaborative AI approach that can play a vital role in intelligent IoT applications. This article provides a comprehensive survey of the emerging applications of FL in IoT networks and highlights its potential in various fields, paving the way for future research directions.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2021)

添加到收藏夹

Article Computer Science, Information Systems