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, +9 more
core +1 more source
Dynamic survival risk prediction with time‐varying high‐dimensional images
Canadian Journal of Statistics, EarlyView.Abstract Integrating longitudinal data with survival models is a prevalent strategy for dynamic survival risk prediction while accounting for subjects' longitudinally observed variables. However, existing methods primarily focus on scalar longitudinal data and seldom tackle the complexities associated with high‐dimensional longitudinal imaging data ...
Bingfan Liu +7 more
wiley +1 more source
Copula‐based joint modelling of emergency department visits with time‐varying dependence
Canadian Journal of Statistics, EarlyView.Abstract Jointly modelling multiple correlated count time series is essential in health services research, where outcomes like emergency visits for mental health and substance use often evolve together. Ignoring these dependencies can obscure meaningful trends and limit the effectiveness of policy evaluation.
Guanjie Lyu, Cindy Feng, Lihui Liu
wiley +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
Vine copula knockoffs for variable selection in gene expression studies
Canadian Journal of Statistics, EarlyView.Abstract Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection.
José Ulises Márquez Urbina +3 more
wiley +1 more source
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 +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 +5 more
core
Canadian Journal of Statistics, EarlyView.Abstract Homogeneous normalized random measures with independent increments represent a broad class of Bayesian nonparametric priors and thus are widely used. In this article, we obtain the strong law of large numbers, the central limit theorem (CLT), and the functional central limit theorem (fCLT) of such measures when the concentration parameter a ...
Junxi Zhang, Shui Feng, Yaozhong Hu
wiley +1 more source
CHPFL: Clustered adaptive hierarchical federated learning for edge-level personalization
High-Confidence ComputingFederated learning faces challenges with non-IID data distributions, often resulting in suboptimal performance for individual clients with the global model. To address this issue, we propose a clustered hierarchical personalized federated learning (CHPFL)
Lihua Song +4 more
doaj +1 more source