Results 61 to 70 of about 31,140,578 (184)

ProFed: A Benchmark for Proximity-Based Non-IID Federated Learning

Journal of Open Research Software
Federated Learning (FL) has emerged as a key paradigm in machine learning but its performance often deteriorates under non-independent and identically distributed (non-IID) client data.
Davide Domini, Christian Otte Ingemann, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli   +4 more
doaj   +1 more source

Non-IID representation learning on complex categorical data [PDF]

, 2019
University of Technology Sydney. Faculty of Engineering and Information Technology.Learning complex categorical data requires proper vector or metric representations of the intricate characteristics of that data.
Zhu, Chengzhang
core  

Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning

Computers
Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem ...
Huda Kadhim Tayyeh, Ahmed Sabah Ahmed AL-Jumaili   +1 more
doaj   +1 more source

Adding Data Quality to Federated Learning Performance Improvement

IEEE Access
Massive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (
Ernesto Gurgel Valente Neto, Solon Alves Peixoto, Valderi Reis Quietinho Leithardt, Juan Francisco de Paz Santana, Julio C. S. Dos Anjos   +4 more
doaj   +1 more source

FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning [PDF]

Logical Methods in Computer Science
In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and performance face significant challenges in real-world deployments where data ...
Davide Domini, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli   +3 more
doaj   +1 more source

Mitigating Backdoor Attacks in Federated Learning Systems Under Non‑IID Data: A Comprehensive Survey

IJCI International Journal of Computers and Information
Federated Learning (FL) enables collaborative model training while preserving data privacy, but its decentralized nature exposes it to backdoor attacks, where malicious clients inject poisoned updates that embed hidden triggers into the global model ...
Ahmed Soliman, Khalid Amin, Noura Semary, Hayam Mousa   +3 more
doaj   +1 more source

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Applied Sciences
Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges.
Basmah Alotaibi, Fakhri Alam Khan, Sajjad Mahmood   +2 more
doaj   +1 more source

Realized Variance and IID Market Microstructure Noise [PDF]

We analyze the properties of a bias-corrected realized variance (RV) in the presence of iid market microstructure noise. The bias correction is based on the first-order autocorrelation of intraday returns and we derive the optimal sampling frequency as ...
Asger Lunde, Peter Reinhard Hansen
core  

The Non-IID Data Quagmire of Decentralized Machine Learning

CoRR, 2019
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a significant challenge to decentralized learning because their different contexts result in significant data distribution skew across devices/locations.
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons   +3 more
openaire   +3 more sources

KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data

Neuroscience Informatics
Data Heterogeneity or Non-IID (non-independent and identically distributed) data identification is one of the prominent challenges in Federated Learning (FL).
Md. Rahad, Ruhan Shabab, Mohd. Sultan Ahammad, Md. Mahfuz Reza, Amit Karmaker, Md. Abir Hossain   +5 more
doaj   +1 more source