Results 91 to 100 of about 14,306 (259)

Cross-Domain Federated Data Modeling on Non-IID Data. [PDF]

Comput Intell Neurosci, 2022
Chai B, Liu K, Yang R.
europepmc   +1 more source

Non-iid hypothesis testing: from classical to quantum

CoRR
We study hypothesis testing (aka state certification) in the non-identically distributed setting. A recent work (Garg et al. 2023) considered the classical case, in which one is given (independent) samples from $T$ unknown probability distributions $p_1, \dots, p_T$ on $[d] = \{1, 2, \dots, d\}$, and one wishes to accept/reject the hypothesis that ...
de Palma, Giacomo, Fanizza, Marco, Mowry, Connor, O'Donnell, Ryan   +3 more
openaire   +3 more sources

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

Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks

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   +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

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. We introduce entropy-regularized federated optimization (ERFO), a lightweight client-side modification that augments each local objective with a Shannon entropy penalty on the per-parameter update ...
openaire   +1 more source

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

Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics. [PDF]

Sensors (Basel), 2023
Hsu HY, Keoy KH, Chen JR, Chao HC, Lai CF.   +4 more
europepmc   +1 more source

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

Federated Learning for Breast Cancer Classification: A Comparative Study of Aggregation Methods

Information
Federated 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, Medjeded Merati, Saïd Mahmoudi   +2 more
doaj   +1 more source