|
|
|
|
|
Jadil Alsamiri and Khalid Alsubhi
In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort....
ver más
|
|
|
|
|
|
|
|
|
|
|
Mikael Sabuhi, Petr Musilek and Cor-Paul Bezemer
As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges a...
ver más
|
|
|
|
|
|
|
|
|
|
|
Rezak Aziz, Soumya Banerjee, Samia Bouzefrane and Thinh Le Vinh
The trend of the next generation of the internet has already been scrutinized by top analytics enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the global population will have their personal data covered under priva...
ver más
|
|
|
|
|
|
|
|
|
|
|
Afsana Khan, Marijn ten Thij and Anna Wilbik
Federated learning (FL) is a privacy-preserving distributed learning approach that allows multiple parties to jointly build machine learning models without disclosing sensitive data. Although FL has solved the problem of collaboration without compromisin...
ver más
|
|
|
|
|
|
|
|
|
|
|
David Naseh, Mahdi Abdollahpour and Daniele Tarchi
This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed in...
ver más
|
|
|
|
|
|
|
|
|
|
|
Changhao Wu, Siyang He, Zengshan Yin and Chongbin Guo
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Liangkun Yu, Xiang Sun, Rana Albelaihi and Chen Yi
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the co...
ver más
|
|
|
|
|
|
|
|
|
|
|
Kavitha Srinivasan, Sainath Prasanna, Rohit Midha, Shraddhaa Mohan
Pág. 1 - 20
Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fields...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zacharias Anastasakis, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Stavroula Bourou, Konstantinos Psychogyios, Dimitrios Skias and Theodore Zahariadis
Federated Learning is identified as a reliable technique for distributed training of ML models. Specifically, a set of dispersed nodes may collaborate through a federation in producing a jointly trained ML model without disclosing their data to each othe...
ver más
|
|
|
|
|
|
|
|
|
|
|
Muneerah Al Asqah and Tarek Moulahi
The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lu Han, Xiaohong Huang, Dandan Li and Yong Zhang
In the ring-architecture-based federated learning framework, security and fairness are severely compromised when dishonest clients abort the training process after obtaining useful information. To solve the problem, we propose a Ring- architecture-based ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Matin Mortaheb, Cemil Vahapoglu and Sennur Ulukus
Multi-task learning (MTL) is a paradigm to learn multiple tasks simultaneously by utilizing a shared network, in which a distinct header network is further tailored for fine-tuning for each distinct task. Personalized federated learning (PFL) can be achi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ahmed A. Al-Saedi, Veselka Boeva and Emiliano Casalicchio
Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To ad...
ver más
|
|
|
|
|
|
|
|
|
|
|
Muhammad Asad, Muhammad Aslam, Syeda Fizzah Jilani, Saima Shaukat and Manabu Tsukada
Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications...
ver más
|
|
|
|
|
|
|
|
|
|
|
Aristeidis Karras, Christos Karras, Konstantinos C. Giotopoulos, Dimitrios Tsolis, Konstantinos Oikonomou and Spyros Sioutas
Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent chall...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shao-Ming Lee and Ja-Ling Wu
Recently, federated learning (FL) has gradually become an important research topic in machine learning and information theory. FL emphasizes that clients jointly engage in solving learning tasks. In addition to data security issues, fundamental challenge...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tongyang Xu, Yuan Liu, Zhaotai Ma, Yiqiang Huang and Peng Liu
As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogen...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lang Wu, Weijian Ruan, Jinhui Hu and Yaobin He
Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable a...
ver más
|
|
|
|
|
|
|
|
|
|
|
Duy-Dong Le, Anh-Khoa Tran, Minh-Son Dao, Kieu-Chinh Nguyen-Ly, Hoang-Son Le, Xuan-Dao Nguyen-Thi, Thanh-Qui Pham, Van-Luong Nguyen and Bach-Yen Nguyen-Thi
The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality a...
ver más
|
|
|
|
|
|
|
|
|
|
|
Hanyue Xu, Kah Phooi Seng, Jeremy Smith and Li Minn Ang
In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the co...
ver más
|
|
|
|
|
|