Results 71 to 80 of about 33,062,051 (187)

Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning approach

, 2022
Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning ...
Mohamed Abdel-Basset (5152634), Hossam Hawash (18173107), Nour Moustafa (9644231), Mohamed Abd Elfattah (18173110), Imran Razzak (13074405)   +4 more
core  

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,, Lenz, Christian, Lenz, Christian,, Nachtigall, Robin,, Awayshehl, Feras M.,, Awayshehl, Feras M., Alawadi, Sadi,, Haller, Marc, Alawadi, Sadi, Nachtigall, Robin   +9 more
core   +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

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

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

Federated PAC-Bayesian Learning on Non-IID data [PDF]

, 2023
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in
Zhao, Zihao, Liu, Yang, Ding, Wenbo, Zhang, Xiao-Ping   +3 more
core   +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

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

Pretraining Client Selection Algorithm Based on a Data Distribution Evaluation Model in Federated Learning

IEEE Access
Federated Learning (FL) allows task initiators (servers) to utilize data from task participants (clients) to train machine learning models while protecting data privacy.
Chang Xu, Hong Liu, Kexin Li, Wanglei Feng, Wei Qi   +4 more
doaj   +1 more source

Cross-Gradient Aggregation for Decentralized Learning from Non-IID data [PDF]

, 2021
Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server.
Esfandiari, Yasaman, Hegde, Chinmay, Tan, Sin Yong, Jiang, Zhanhong, Balu, Aditya, Herron, Ethan, Sarkar, Soumik   +6 more
core