Results 211 to 220 of about 72,357 (237)

When Federated Learning Meets Privacy-Preserving Computation

ACM Computing Surveys
Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide attention from society and individuals. It is desirable to make the data available but invisible, i.e., to realize data analysis and calculation without ...
Jingxue Chen, Hang Yan, Zhiyuan Liu, Min Zhang, Hu Xiong, Shui Yu   +5 more
semanticscholar   +1 more source

Federated Learning-Based Misbehavior Detection for the 5G-Enabled Internet of Vehicles

IEEE transactions on consumer electronics
The concept of federated learning (FL) is becoming increasingly popular as a method for training collaborative models without loss the sensitive information.
Preeti Rani, Chandani Sharma, J. V. N. Ramesh, Sonia Verma, Rohit Sharma, A. Alkhayyat, Sachin Kumar   +6 more
semanticscholar   +1 more source

Federated Learning for Healthcare Applications

IEEE Internet of Things Journal
Due to the fast advancement of artificial intelligence (AI), centralized-based models have become critical for healthcare tasks like in medical image analysis and human behavior recognition.
A. Chaddad, Yihang Wu, Christian Desrosiers   +2 more
semanticscholar   +1 more source

Differentially Private Federated Learning With an Adaptive Noise Mechanism

IEEE Transactions on Information Forensics and Security
Federated Learning (FL) enables multiple distributed clients to collaboratively train a model with owned datasets. To avoid the potential privacy threat in FL, researchers propose the DP-FL strategy, which utilizes differential privacy (DP) to add ...
Rui Xue, Kaiping Xue, Bin Zhu, Xin Luo, Tianwei Zhang, Qibin Sun, Jun Lu   +6 more
semanticscholar   +1 more source

Computation and Communication Efficient Federated Learning With Adaptive Model Pruning

IEEE Transactions on Mobile Computing
Federated learning (FL) has emerged as a promising distributed learning paradigm that enables a large number of mobile devices to cooperatively train a model without sharing their raw data.
Zhida Jiang, Yang Xu, Hong-Ze Xu, Zhiyuan Wang, Jianchun Liu, Qian Chen, C. Qiao   +6 more
semanticscholar   +1 more source

DetFed: Dynamic Resource Scheduling for Deterministic Federated Learning Over Time-Sensitive Networks

IEEE Transactions on Mobile Computing
In this paper, we present a three-layer (i.e., device, field, and factory layers) deterministic federated learning (FL) framework, named DetFed, which accelerates collaborative learning process for ultra-reliable and low-latency industrial Internet of ...
Dong Yang, Weiting Zhang, Qiang Ye, Chuan Zhang, N. Zhang, C. Huang, Hongke Zhang, Xuemin Shen   +7 more
semanticscholar   +1 more source

Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief Survey

IEEE Internet of Things Journal
Federated learning (FL) offers distributed machine learning on edge devices. However, the FL model raises privacy concerns. Various techniques, such as homomorphic encryption (HE), differential privacy, and multiparty cooperation, are used to address the
Qipeng Xie, Siyang Jiang, Linshan Jiang, Yongzhi Huang, Zhihe Zhao, Salabat Khan, Wangchen Dai, Zhe Liu, Kaishun Wu   +8 more
semanticscholar   +1 more source

FL-PERF: Predicting TCP Throughput with Federated Learning

GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023
Han Nay Aung, Hiroyuki Ohsaki
openaire   +1 more source

Sunday-FL – Developing Open Source Platform for Federated Learning

2021 Emerging Trends in Industry 4.0 (ETI 4.0), 2021
Niedziela, Piotr, Danilenka, Anastasiya, Kolasa, Dominik, Ganzha, Maria, Paprzycki, Marcin, Nalinaksh, Kumar   +5 more
openaire   +1 more source

PROV-FL: Privacy-preserving Round Optimal Verifiable Federated Learning

Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security, 2022
Vishnu Asutosh Dasu, Sumanta Sarkar, Kalikinkar Mandal   +2 more
openaire   +1 more source