Results 21 to 30 of about 31,140,529 (283)

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 XGBoost on Sample-Wise Non-IID Data

CoRR, 2022
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution of the local parties involved in the training process.
Katelinh Jones, Yuya Jeremy Ong, Yi Zhou 0015, Nathalie Baracaldo   +3 more
openaire   +3 more sources

Fast converging Federated Learning with Non-IID Data

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023
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. In traditional ML, data are sent to a server for training. However, this approach raises user privacy concerns.
Sigg Stephan, Naas Si Ahmed
openaire   +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

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

Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data [PDF]

Sensors, 2023
Taejoon Kim
exaly   +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

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

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