Results 131 to 140 of about 34,864,804 (179)

Blockchain-Based Two-Stage Federated Learning With Non-IID Data in IoMT System

IEEE Transactions on Computational Social Systems, 2023
The Internet of Medical Things (IoMT) has a bright future with the development of smart mobile devices. Information technology is also leading changes in the healthcare industry.
Zhuo-Tao Lian, Qingkui Zeng, Wei-Zheng Wang, T. Gadekallu, Chunhua Su   +4 more
semanticscholar   +1 more source

Federated Learning With Adaptive Aggregation Weights for Non-IID Data in Edge Networks

IEEE Transactions on Cognitive Communications and Networking
Federated learning (FL) enables edge nodes to collaboratively train a global model under the coordination of a server without sharing local private data.
Xiaodong Li, Yu-Long Gao, Yan-Sha Deng, Xinzhuo Jiang   +3 more
semanticscholar   +1 more source

FedSiam-DA: Dual-Aggregated Federated Learning via Siamese Network for Non-IID Data

IEEE Transactions on Mobile Computing
Federated learning (FL) is an effective mobile edge computing framework that enables multiple participants to collaboratively train intelligent models, without requiring large amounts of data transmission while protecting privacy.
Xin Wang, Yan-Han Wang, Ming Yang, Feng Li, Xiao-Ming Wu, Li-Sheng Fan, Shi-Bo He   +6 more
semanticscholar   +1 more source

Long-Term Client Selection for Federated Learning With Non-IID Data: A Truthful Auction Approach

IEEE Internet of Things Journal
Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Internet of Vehicles (IoV).
Jinghong Tan, Zhian Liu, Kun Guo, Ming-Xiong Zhao   +3 more
semanticscholar   +1 more source

HeteroSFL: Split Federated Learning With Heterogeneous Clients and Non-IID Data

IEEE Internet of Things Journal
Split Federated Learning (SFL) is an emerging privacy-preserving decentralized learning scheme which splits a machine learning model between client and server such that most of the computations are offloaded to the server.
Xing Chen, Jingtao Li, Deliang Fan, Chaitali Chakrabarti   +3 more
semanticscholar   +1 more source

FedLFP: Communication-Efficient Personalized Federated Learning on Non-IID Data in Mobile Edge Computing Environments

IEEE Transactions on Mobile Computing
Mobile Edge Computing (MEC) facilitates computing and storage at edge nodes near user devices, reducing latency and optimizing bandwidth. Federated Learning (FL) complements MEC by enabling privacy-preserving collaborative model training across edge ...
Yu-Xia Sun, Siyi Pan, Aoxiang Sun, Zhixiao Fu, Sai-Qin Long, Zhe-Tao Li   +5 more
semanticscholar   +1 more source

Federated Learning Approaches for IoT Intrusion Detection Based on FedAvg and FedProx on IID and Non-IID Data

International Conference on Information and Communicatiaon Technology
The increasing complexity of cyberattacks in the Internet of Things (IoT) architecture demands an Intrusion Detection System (IDS) that is not only accurate but also maintains data privacy.
Satria W. Herlambang, Favian Dewanta, Yudha Purwanto   +2 more
semanticscholar   +1 more source

Efficient Split Learning with Non-iid Data

2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022
Yuanqin Cai, Tongquan Wei
openaire   +1 more source

Improving Federated Transfer Learning for Different Levels of Non-IID Data with Multi-Round Transfer and Forgetting Mitigation

ICC 2025 - IEEE International Conference on Communications
Federated Learning (FL) enhances data privacy by allowing clients to share only model parameters. However, it suffers from slow convergence speed and low accuracy under non-Independent and Identically Distributed (non-IID) data conditions due to the ...
Bo-Yuan Zhang, M. Shikh-Bahaei
semanticscholar   +1 more source

Kernel Measures of Independence for non-iid Data.

2009
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures using undirected graphical models and comparing the Hilbert space
Zhang, X., Song, L., Gretton, A., Smola, A.   +3 more
openaire   +2 more sources