Results 71 to 80 of about 31,140,578 (184)

Pretraining Client Selection Algorithm Based on a Data Distribution Evaluation Model in Federated Learning

IEEE Access
Federated Learning (FL) allows task initiators (servers) to utilize data from task participants (clients) to train machine learning models while protecting data privacy.
Chang Xu, Hong Liu, Kexin Li, Wanglei Feng, Wei Qi   +4 more
doaj   +1 more source

Cloud–Edge–End Collaborative Federated Learning: Enhancing Model Accuracy and Privacy in Non-IID Environments

Sensors
Cloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID ...
Ling Li, Lidong Zhu, Weibang Li
doaj   +1 more source

Analysis and Improvement of Entropy Estimators in NIST SP 800-90B for Non-IID Entropy Sources

IACR Transactions on Symmetric Cryptology, 2017
Random number generators (RNGs) are essential for cryptographic applications. In most practical applications, the randomness of RNGs is provided by entropy sources.
Shuangyi Zhu, Yuan Ma, Tianyu Chen, Jingqiang Lin, Jiwu Jing   +4 more
doaj   +1 more source

Non-IID scenario: 10-fold cross validation results with varying C.

, 2020
Non-IID scenario: 10-fold cross validation results with varying C.
Zeng Fu (8727135), Dianbo Liu (8311149), Li Huang (107408), Yifeng Yin (2992161), Hao Deng (409186), Shifa Zhang (8727138)   +5 more
core   +1 more source

Handling Non-IID Data in Federated Learning : An Experimental Evaluation Towards Unified Metrics

, 2023
Recent research has demonstrated that Non-Identically Distributed (Non-IID) data can negatively impact the performance of global models constructed in federated learning. To address this concern, multiple approaches have been developed.
Haller, Marc,, Lenz, Christian, Lenz, Christian,, Nachtigall, Robin,, Awayshehl, Feras M.,, Awayshehl, Feras M., Alawadi, Sadi,, Haller, Marc, Alawadi, Sadi, Nachtigall, Robin   +9 more
core   +1 more source

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

Sensors (Basel), 2023
Seol M, Kim T.
europepmc   +1 more source

Global Convergence of Continual Learning on Non-IID Data

CoRR
Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical aspect remains under-explored. Recently, a few investigations have considered the theory of continual learning only for linear regressions, establishes the results based on ...
Fei Zhu 0004, Yujing Liu, Wenzhuo Liu, Zhaoxiang Zhang 0001   +3 more
openaire   +3 more sources

DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data. [PDF]

Sensors (Basel), 2022
Lee J, Kim W.
europepmc   +1 more source

Improving Non-IID federated survival analysis with data augmentation and gradient boosted trees

Data-driven machine learning models have increasingly been applied to survival analysis in recent years. However, these models require sufficient training samples, which is often impractical due to privacy, security, and legal constraints.
Wang, H, Zou, Y, Huang, Y, Ng, SK, Zhang, X   +4 more
core   +1 more source

Feature Matching Data Synthesis for Non-IID Federated Learning

, 2023
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server.
Sun, Yuchang, Mao, Yuyi, Shao, Jiawei, Wang, Jessie Hui, Li, Zijian, Zhang, Jun   +5 more
core