Results 11 to 20 of about 31,140,578 (184)

Entropy-Regularized Federated Optimization for Non-IID Data

Algorithms
Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance.
Koffka Khan
doaj   +2 more sources

Federated multimodal malware classification under non-IID data

Cybersecurity
Malware data in real-world cybersecurity applications are typically distributed across multiple organizations, and privacy, security, and compliance constraints prevent these data from being shared with a central server.
Shaohua Liu, Wenbo Zhang, Tingting Yang, Guanghao Li, Lihua Zhang   +4 more
doaj   +2 more sources

Fast converging Federated Learning with Non-IID Data

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023
Publisher Copyright: © 2023 IEEE.With the advancement of device capabilities, Internet of Things (IoT) devices can employ built-in hardware to perform machine learning (ML) tasks, extending their horizons in many promising directions.
Sigg, Stephan, Naas, Si Ahmed
core   +3 more sources

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Engineering, 2016
While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services.
Longbing Cao
exaly   +3 more sources

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

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

Cross-Domain Federated Data Modeling on Non-IID Data. [PDF]

Comput Intell Neurosci, 2022
Federated learning has received sustained attention in recent years for its distributed training model that fully satisfies the need for privacy concerns. However, under the nonindependent identical distribution, the data heterogeneity of different parties with different data patterns significantly degrades the prediction performance of the federated ...
Chai B, Liu K, Yang R.
europepmc   +3 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
The 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 is independent and identically distributed (IID). However,
Weiss, Jeremy C., Walsh, Toby, Zhang, Wenbin, Zhou, Shuigeng   +3 more
core   +4 more sources