Results 121 to 130 of about 31,140,529 (283)

Differentially Private Federated Clustering Over Non-IID Data

IEEE Internet of Things Journal
34 pages, 4 figures, 1 ...
Yiwei Li 0003, Shuai Wang 0033, Chong-Yung Chi, Tony Q. S. Quek   +3 more
openaire   +2 more sources

Complex Versus Parsimonious Site‐Based Stochastic Ground Motion Models: Which One Is Better?

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Stochastic ground motion models (GMMs) provide a probabilistic representation of seismic input and are increasingly important for uncertainty quantification (UQ) in earthquake engineering. This study focuses on site‐based stochastic GMMs, which learn the statistical features of selected datasets of seismic records and generate statistically ...
Maijia Su, Mayssa Dabaghi, Marco Broccardo   +2 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  

Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model

Journal of Forecasting, EarlyView.
ABSTRACT This paper introduces a Threshold Asymmetric Conditional Autoregressive Range (TACARR) model for analyzing the daily price ranges of financial assets. The proposed formulation assumes that the conditional expected range switches between two regimes, representing upward and downward market states, with the disturbance distribution also allowed ...
Isuru Ratnayake, V. A. Samaranayake
wiley   +1 more source

A thorough assessment of the non-IID data impact in federated learning

Journal of Industrial Information Integration
Federated 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, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti   +4 more
openaire   +5 more sources

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

Forecasting Duration in High‐Frequency Financial Data Using a Self‐Exciting Flexible Residual Point Process

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

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, Gianluigi Semeraro, Davide Cantoro, Angela-Tafadzwa Shumba, Teodoro Montanaro, Ilaria Sergi, Luigi Patrono   +6 more
core   +1 more source

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, He, Tianyu, Duan, Shaoming, Liu, Chuanyi, Deng, Qiyuan, Xu, Yifeng   +5 more
core  

The Role of Variance Risk Premium in Derivative Pricing: Modeling, Estimation and Impact

Journal of Futures Markets, EarlyView.
ABSTRACT This paper estimates a model where variance risk premiums (VRP) is not fully explained by equity risk premiums (ERP). This separation can be detected thanks to a new breed of GARCH models with enough innovations to disconnect returns from variances. This type of risk‐neutralization is compatible with continuous‐time settings.
Marcos Escobar‐Anel, Lars Stentoft, Xize Ye   +2 more
wiley   +1 more source