Federated Learning Architecture

Federated learning is a distributed machine learning approach designed to train models on decentralized data without directly sharing sensitive information. Current research focuses on improving efficiency and security through various architectures, including centralized and decentralized models, hierarchical structures with submodel partitioning, and the integration of techniques like homomorphic encryption. This approach is particularly significant for applications requiring data privacy, such as healthcare and IoT, enabling collaborative model training while adhering to data protection regulations and enhancing the robustness of models trained on heterogeneous data.

Papers

September 14, 2024

Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
Siddhant Dutta, Pavana P Karanth, Pedro Maciel Xavier, Iago Leal de Freitas, Nouhaila Innan, Sadok Ben Yahia, Muhammad Shafique, David E. Bernal Neira
Machine Learning Quantum Computing Homomorphic Encryption Computing Paradigm Federated Learning Architecture

August 6, 2024

Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application
Anwesha Mukherjee, Rajkumar Buyya
Long Short Term Memory Performance Evaluation Crop Yield Prediction Decentralized Framework Federated Learning Architecture Centralized Federated Learning

February 15, 2024

An advanced data fabric architecture leveraging homomorphic encryption and federated learning
Sakib Anwar Rieyan, Md. Raisul Kabir News, A. B. M. Muntasir Rahman, Sadia Afrin Khan, Sultan Tasneem Jawad Zaarif, Md. Golam Rabiul Alam, Mohammad Mehedi Hassan, Michele Ianni, Giancarlo Fortino
LeArning Abstract Medical Image Homomorphic Encryption Privacy Preserving Machine Learning Dataflow Accelerator Federated Learning Architecture

October 27, 2023

Submodel Partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis
Wenzhi Fang, Dong-Jun Han, Christopher G. Brinton
Convergence Analysis Non Convex Loss Function Hierarchical Federated Learning Algorithm Design Federated Learning Architecture Model Partition Submodel Extraction

September 27, 2022

May 11, 2022

Secure & Private Federated Neuroimaging
Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite
Private Data Secure Approach Global Deep Federated Learning Architecture

April 28, 2022

Decision Models for Selecting Federated Learning Architecture Patterns
Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu
Human Decision Machine Learning Architecture Federated Machine Learning Federated Learning Architecture

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 Architecture

Papers

Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML

Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application

An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Submodel Partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis

A Snapshot of the Frontiers of Client Selection in Federated Learning

FedStack: Personalized activity monitoring using stacked federated learning

Secure & Private Federated Neuroimaging

Decision Models for Selecting Federated Learning Architecture Patterns

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