Federated Learning Approach

Federated learning (FL) is a decentralized machine learning approach enabling multiple parties to collaboratively train a shared model without directly sharing their data, thereby addressing privacy concerns. Current research focuses on improving FL's performance in heterogeneous data settings, employing techniques like influence-oriented aggregation and adaptive feature mixing to enhance accuracy and efficiency across diverse model architectures, including neural networks (e.g., MLPs, LSTMs) and kernel methods. This approach holds significant promise for various applications, from personalized healthcare and smart grids to water conservation and offensive language detection, by enabling the development of robust and accurate models while safeguarding sensitive data.

Papers

November 7, 2024

DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging
Prakash Chourasia, Tamkanat E Ali, Sarwan Ali, Murray Pattersn
Federated Learning Approach Protein Classification

October 17, 2024

Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach
Luyao Zou, Yu Min Park, Chu Myaet Thwal, Yan Kyaw Tun, Zhu Han, Choong Seon Hong
Multi Stage Spectral Feature Multiple Satellite Federated Learning Approach Satellite Data Satellite Constellation

October 4, 2024

Influence-oriented Personalized Federated Learning
Yue Tan, Guodong Long, Jing Jiang, Chengqi Zhang
Federated Learning Federated Learning Approach Influence Analysis

August 21, 2024

August 18, 2024

Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets
Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Li Shen, Puning Zhao, Jie Xu, Han Hu
Data Set Non Convex Loss Function Byzantine Robust Federated Learning Federated Learning Approach Optimality Gap Federated Stochastic Byzantine Robust Method

July 20, 2024

Universal Medical Imaging Model for Domain Generalization with Data Privacy
Ahmed Radwan, Islam Osman, Mohamed S. Shehata
Training Data Medical Image Domain Generalization Data Privacy Private Datasets Federated Learning Approach Medical Imaging Task

April 27, 2024

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
Liping Yi, Han Yu, Chao Ren, Heng Zhang, Gang Wang, Xiaoguang Liu, Xiaoxiao Li
Heterogeneous Federated Learning Scalable Personalization Federated Learning Approach Personalized Prediction Adaptive Mixture Local Heterogeneous Model

April 17, 2024

A Federated Learning Approach to Privacy Preserving Offensive Language Identification
Marcos Zampieri, Damith Premasiri, Tharindu Ranasinghe
Social Medium Data Offensive Content Offensive Language Offensive Language Detection Federated Learning Approach

January 22, 2024

Attention on Personalized Clinical Decision Support System: Federated Learning Approach
Chu Myaet Thwal, Kyi Thar, Ye Lin Tun, Choong Seon Hong
Human Attention Decision Support System Clinical Decision Support System Smart Healthcare Federated Learning Approach Long Clinical

January 14, 2024

The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting
Shuyao Zhang, Jordan Tay, Pedro Baiz
Mixed Effect Risk Prediction Data Imbalance Imbalanced Datasets Credit Risk Federated Learning Approach

November 12, 2023

pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing
Liping Yi, Han Yu, Gang Wang, Xiaoguang Liu
Privacy Preserving Local Model Feature Extractor Federated Learning Approach Local Heterogeneous Model

November 10, 2023

Blockchain-Enabled Federated Learning Approach for Vehicular Networks
Shirin Sultana, Jahin Hossain, Maruf Billah, Hasibul Hossain Shajeeb, Saifur Rahman, Keyvan Ansari, Khondokar Fida Hasan
Vehicular Network Federated Learning Approach

November 4, 2023

Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis
Luca Lusnig, Asel Sagingalieva, Mikhail Surmach, Tatjana Protasevich, Ovidiu Michiu, Joseph McLoughlin, Christopher Mansell, Graziano de' Petris, Deborah Bonazza, Fabrizio Zanconati, Alexey Melnikov, Fabio Cavalli
LeArning Abstract Quantum Machine Learning Federated Learning Approach Non Alcoholic Fatty Liver Disease

August 31, 2023

Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Lei Bai, Dongang Wang, Michael Barnett, Mariano Cabezas, Weidong Cai, Fernando Calamante, Kain Kyle, Dongnan Liu, Linda Ly, Aria Nguyen, Chun-Chien Shieh, Ryan Sullivan, Hengrui Wang, Geng Zhan, Wanli Ouyang, Chenyu Wang
Federated Learning Multiple Sclerosis Federated Learning Approach Label Correction Multi Site Data Clinical Trial Site

June 5, 2023

A Privacy-Preserving Federated Learning Approach for Kernel methods
Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan, Erik Buchmann, Mete Akgün
Privacy Preserving Kernel Method Kernel Matrix Federated Learning Approach Kernel Machine

May 31, 2023

FedCSD: A Federated Learning Based Approach for Code-Smell Detection
Sadi Alawadi, Khalid Alkharabsheh, Fahed Alkhabbas, Victor Kebande, Feras M. Awaysheh, Fabio Palomba, Mohammed Awad
Training Data Centralized Training Federated Learning Approach Code Smell

May 22, 2023

Federated Learning of Medical Concepts Embedding using BEHRT
Ofir Ben Shoham, Nadav Rappoport
Electronic Health Record Federated Learning Approach Biomedical Concept

April 7, 2023

FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination
Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser Hosseinzadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
Federated Learning Unknown Disturbance Federated Learning Approach

November 10, 2022

Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication
Mohamad Wazzeh, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Mohsen Guizani
Machine Learning Federated Learning Formality Transfer Identity Verification Federated Learning Approach Behavioral Biometrics Warmup Period

Federated Learning Approach

Papers

DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging

Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach

Influence-oriented Personalized Federated Learning

Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data

Federated Learning Approach to Mitigate Water Wastage

Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets

Universal Medical Imaging Model for Domain Generalization with Data Privacy

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning

A Federated Learning Approach to Privacy Preserving Offensive Language Identification

Attention on Personalized Clinical Decision Support System: Federated Learning Approach

The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting

pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing

Blockchain-Enabled Federated Learning Approach for Vehicular Networks

Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis

Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training

A Privacy-Preserving Federated Learning Approach for Kernel methods

FedCSD: A Federated Learning Based Approach for Code-Smell Detection

Federated Learning of Medical Concepts Embedding using BEHRT

FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination

Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication