Federated Learning Model

Federated learning (FL) is a distributed machine learning approach enabling collaborative model training across multiple devices or institutions without directly sharing sensitive data. Current research emphasizes addressing challenges posed by data heterogeneity across participating entities, focusing on techniques like improved aggregation algorithms (e.g., using optimization methods like Whale Optimization Algorithm) and robust model architectures (e.g., closed-form classifiers, Bayesian neural networks, and latent class regression) to enhance accuracy and convergence speed. FL's significance lies in its ability to leverage large, decentralized datasets for diverse applications, including healthcare, renewable energy prediction, and electric vehicle management, while upholding data privacy.

Papers

December 24, 2024

An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack
Kunal Bhatnagar, Sagana Chattanathan, Angela Dang, Bhargav Eranki, Ronnit Rana, Charan Sridhar, Siddharth Vedam, Angie Yao, Mark Stamp
Adversarial Attack Empirical Analysis Adversarial Behavior Adversarial Agent Label Flipping Attack Federated Learning Model

November 19, 2024

Attribute Inference Attacks for Federated Regression Tasks
Francesco Diana, Othmane Marfoq, Chuan Xu, Giovanni Neglia, Frédéric Giroire, Eoin Thomas
Inference Attack Reconstruction Attack Attribute Inference Attack Federated Learning Model

October 28, 2024

A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
Saptarshi Chakraborty, Peter L. Bartlett
Generalization Error Generalization Property Intrinsic Dimension Statistical Analysis Federated Deep Low Dimensional Data Federated Learning Model

June 3, 2024

Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Eros Fanì, Raffaello Camoriano, Barbara Caputo, Marco Ciccone
Heterogeneous Federated Learning Ridge Regression Closed Set Federated Learning Model

April 9, 2024

Federated learning model for predicting major postoperative complications
Yonggi Park, Yuanfang Ren, Benjamin Shickel, Ziyuan Guan, Ayush Patela, Yingbo Ma, Zhenhong Hu, Tyler J. Loftus, Parisa Rashidi, Tezcan Ozrazgat-Baslanti, Azra Bihorac
Federated Learning Intraoperative Image Postoperative Complication Federated Learning Model

September 19, 2023

FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction
Viorica Chifu, Tudor Cioara, Cristian Anitiei, Cristina Pop, Ionut Anghel
Energy Consumption Renewable Energy Whale Optimization Algorithm Energy Data Federated Learning Model

July 18, 2023

A Federated learning model for Electric Energy management using Blockchain Technology
Muhammad Shoaib Farooq, Azeen Ahmed Hayat
Energy Management Blockchain Technology Ledger Technology Residential Prosumers Federated Learning Model

November 14, 2022

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
Shuo Shao, Wenyuan Yang, Hanlin Gu, Zhan Qin, Lixin Fan, Qiang Yang, Kui Ren
Watermarking Method Ownership Verification Copyright Traceability Federated Learning Model

October 24, 2022

Detection and Prevention Against Poisoning Attacks in Federated Learning
Viktor Valadi, Madeleine Englund, Mark Spanier, Austin O'brien
Data Detection Poisoning Attack Effective Prevention Federated Learning Model Deviation Detection

June 22, 2022

Federated Latent Class Regression for Hierarchical Data
Bin Yang, Thomas Carette, Masanobu Jimbo, Shinya Maruyama
Hierarchical Latent Hierarchical Datasets Latent Class Federated Learning Model

June 13, 2022

Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
Haolin Yu, Kaiyang Guo, Mahdi Karami, Xi Chen, Guojun Zhang, Pascal Poupart
Federated Learning Model Federated Gaussian Process

November 13, 2021

An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning
Shiliang Zhang
Vehicular Network Ion Battery Federated Learning Architecture Federated Learning Model Energy Consumption Model Energy Aware

Federated Learning Model

Papers

An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack

Attribute Inference Attacks for Federated Regression Tasks

A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data

Accelerating Heterogeneous Federated Learning with Closed-form Classifiers

Federated learning model for predicting major postoperative complications

FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction

A Federated learning model for Electric Energy management using Blockchain Technology

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

Detection and Prevention Against Poisoning Attacks in Federated Learning

Federated Latent Class Regression for Hierarchical Data

Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process

An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning