Results 121 to 130 of about 31,221,148 (284)
Differentially Private Federated Clustering Over Non-IID Data
IEEE Internet of Things Journal34 pages, 4 figures, 1 ...
Yiwei Li 0003 +3 more
openaire +2 more sources
Epileptic arousals: A neglected clinical entity
Epilepsia, EarlyView.Abstract Objective Epileptic arousals (EAs) are seizures characterized solely by arousal from sleep. EAs are unrecognized in current seizure classifications. Their subtle semiology and inconsistently detectable ictal activity in scalp electroencephalography (EEG) complicate differentiation from physiological arousals (PAs). This study characterizes EAs
Lea Fisel +8 more
wiley +1 more source
Weighted Ensemble Distillation in Federated Learning with Non-IID Data
, 2022 Federated distillation (FD) is a novel algorithmic idea for federated learning (FL) that allows clients to use heterogeneous model architectures. This is achieved by distilling aggregated local model predictions on an unlabeled auxiliary dataset into ...
Eriksson, Oscar
core
A New Implementation of Network GARCH Model for Stock Volatility and Co‐Volatility Forecasting
Journal of Forecasting, EarlyView.ABSTRACT Volatility clustering and spillovers are key features of financial time series with many cross‐sectional assets. While network analysis links similar or correlated stocks and helps trace volatility spillovers, contemporary multivariate ARCH‐GARCH formulations struggle to represent structured network dependence and remain parsimonious.
Peiyi Zhou
wiley +1 more source
Federated Learning for Breast Cancer Classification: A Comparative Study of Aggregation Methods
InformationFederated Learning (FL) allows healthcare institutions to collaboratively develop machine learning models while safeguarding patient data, making it ideal for privacy-sensitive medical imaging.
Nadjat Saàdia Lachemi +2 more
doaj +1 more source
A thorough assessment of the non-IID data impact in federated learning
Journal of Industrial Information IntegrationFederated learning (FL) allows collaborative machine learning (ML) model training among decentralized clients' information, ensuring data privacy. The decentralized nature of FL deals with non-independent and identically distributed (non-IID) data. This open problem has notable consequences, such as decreased model performance and more significant ...
Daniel Mauricio Jimenez Gutierrez +4 more
openaire +5 more sources
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data
, 2023 Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL.
Han, Peiyi +5 more
core
Journal of Forecasting, EarlyView.ABSTRACT This paper presents a method for forecasting limit order book durations using a self‐exciting flexible residual point process. High‐frequency events in modern exchanges exhibit heavy‐tailed interarrival times, posing a significant challenge for accurate prediction.
Kyungsub Lee
wiley +1 more source
The Impact of Economic Growth on Insurance (Growth)
International Journal of Finance &Economics, EarlyView.ABSTRACT This paper investigates the impact of economic growth on insurance (growth), focusing on the outflow side of the insurance activity, as captured by benefits (including commissions and expenses). The findings provide evidence that economic growth does exert a positive, statistically significant impact on the benefit side of insurance (growth ...
Nicholas Apergis +2 more
wiley +1 more source
Analyzing the Impact of Non-IID Data on IoT-Enabled Federated Learning for ECG Arrhythmia Detection
The integration of Federated Learning (FL) in the Internet of Medical Things (IoMT) represents a cutting-edge solution, enabling the training of Artificial Intelligence (AI) models directly on edge devices without the need to share sensitive patient ...
Massimo De Vittorio +6 more
core +1 more source