Results 21 to 30 of about 31,221,148 (284)

Federated Conditional Variational Auto Encoders for Cyber Threat Intelligence: Tackling Non-IID Data in SDN Environments

IEEE Access
Federated Learning is a promising paradigm for sharing Cyber Threat Intelligence (CTI) without privacy issues by leveraging the cross-silos data in Software Defined Networking (SDN).
Syed Hussain Ali Kazmi, Rosilah Hassan, Faizan Qamar, Kashif Nisar, Mohammed Azmi Al-Betar   +4 more
doaj   +2 more sources

Advanced Optimization Techniques for Federated Learning on Non-IID Data

Future Internet
Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy.
Filippos Efthymiadis, Aristeidis Karras, Christos Karras, Spyros Sioutas   +3 more
doaj   +2 more sources

FedProc: Prototypical contrastive federated learning on non-IID data

Future Generation Computer Systems, 2023
Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of all clients are not independent and identically distributed (i.e., non-IID), it is challenging to implement this form of efficient collaborative learning.
Ke Cheng, Yulong Shen, Xutong Mu
exaly   +3 more sources

Addressing Non-IID with Data Quantity Skew in Federated Learning

Information
Non-IID is one of the key challenges in federated learning. Data heterogeneity may lead to slower convergence, reduced accuracy, and more training rounds.
Narisu Cha, Long Chang
doaj   +2 more sources

Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks [PDF]

Scientific Reports
Multi-institutional healthcare Internet of Things (IoT) networks face a core challenge between combined intrusion detection and patient data privacy.
Sudhakar Sengan, Chin-Shiuh Shieh
doaj   +2 more sources

Fairness amidst non‐IID graph data: A literature review

AI Magazine
AbstractThe growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data are independent and identically distributed (IID).
Wenbin Zhang 0002, Jeremy C. Weiss, Shuigeng Zhou, Toby Walsh   +3 more
openaire   +4 more sources

Federated Learning Architecture for Non-IID Data [PDF]

Jisuanji gongcheng, 2023
In the scenarios of federated learning involving ultra-large-scale edge devices, the local data of participants are non-Independent Identically Distribution(non-IID) pattern, resulting in an imbalance in overall training data and difficulty in defending ...
Tianchen QIU, Xiaoying ZHENG, Yongxin ZHU, Songlin FENG
doaj   +1 more source

TsingZ0/PFL-Non-IID: fix bugs

, 2023
Personalized federated learning simulation platform with non-IID and unbalanced ...
Tsing, NewAlexandria, youpengl, Yuwen Yang   +3 more
core   +1 more source

A Graph Neural Network Based Decentralized Learning Scheme

Sensors, 2022
As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices.
Huiguo Gao, Mengyuan Lee, Guanding Yu, Zhaolin Zhou   +3 more
doaj   +1 more source

On the Convergence of FedAvg on Non-IID Data

CoRR, 2019
2020 International Conference on Learning ...
Xiang Li 0050, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang 0004   +4 more
openaire   +3 more sources