Centralized Machine Learning

Centralized machine learning (ML) faces challenges related to data privacy, security, and computational efficiency, prompting research into decentralized alternatives like federated learning. Current research focuses on developing robust and efficient federated learning algorithms, including variations of gradient descent methods and novel aggregation techniques, to address issues like data heterogeneity, communication overhead, and adversarial attacks. This shift towards decentralized approaches is significant because it enables collaborative model training across multiple entities while preserving data privacy and reducing the computational burden on central servers, with applications spanning healthcare, network security, and personalized recommendations.

Papers

August 24, 2023

Federated Learning for Computer Vision
Yassine Himeur, Iraklis Varlamis, Hamza Kheddar, Abbes Amira, Shadi Atalla, Yashbir Singh, Faycal Bensaali, Wathiq Mansoor
Machine Learning Computer Vision Privacy Preservation Centralized Machine Learning

August 16, 2023

FedPop: Federated Population-based Hyperparameter Tuning
Haokun Chen, Denis Krompass, Jindong Gu, Volker Tresp
Federated Prompt Cooperation Hyperparameter Tuning Centralized Machine Learning

June 6, 2023

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters
Zhenyu Sun, Xiaochun Niu, Ermin Wei
Strong Generalization Stochastic Gradient Descent Generalization Performance Core Stability Unobserved Heterogeneity Centralized Machine Learning

May 19, 2023

Trustworthy Federated Learning: A Survey
Asadullah Tariq, Mohamed Adel Serhani, Farag Sallabi, Tariq Qayyum, Ezedin S. Barka, Khaled A. Shuaib
Timely Survey Federated Learning Centralized Machine Learning Comprehensive Trustworthiness

March 26, 2023

Green Federated Learning
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael Rabbat, Carole-Jean Wu, Ilya Mironov
Artificial Intelligence Federated Learning Centralized Machine Learning Carbon Footprint Green AI

March 13, 2023

Network Anomaly Detection Using Federated Learning
William Marfo, Deepak K. Tosh, Shirley V. Moore
Centralized Training Centralized Machine Learning Network Anomaly Detection Efficient Anomaly Detection

December 28, 2022

Proof of Swarm Based Ensemble Learning for Federated Learning Applications
Ali Raza, Kim Phuc Tran, Ludovic Koehl, Shujun Li
Ensemble Learning Centralized Machine Learning Partial Proof Consensus Algorithm Federated Learning Application

December 15, 2022

White-box Inference Attacks against Centralized Machine Learning and Federated Learning
Jingyi Ge
Machine Learning Membership Inference Attack White Box Inference Attack Centralized Machine Learning Neural Network Layer

September 9, 2022

Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
Xin Li, Yao Qiang, Chengyin Li, Sijia Liu, Dongxiao Zhu
Deep Neural Network Financial Application Adversarial Training Medical Image Classification Human Saliency Centralized Machine Learning Generalizable Feature

September 1, 2022

To Store or Not? Online Data Selection for Federated Learning with Limited Storage
Chen Gong, Zhenzhe Zheng, Yunfeng Shao, Bingshuai Li, Fan Wu, Guihai Chen
Data Stream Data Selection Centralized Machine Learning Large Scale Data Device Use Case

August 29, 2022

Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces
Shitong Sun, Chenyang Si, Guile Wu, Shaogang Gong
Foundation Model Semantic Feature Label Space Centralized Machine Learning

June 21, 2022

An Energy and Carbon Footprint Analysis of Distributed and Federated Learning
Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush, Mehdi Bennis
Energy Policy Research Centralized Machine Learning Carbon Footprint Clean Distribution Digital Footprint

May 30, 2022

FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction
Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, Panagiotis Papapetrou
Centralized Machine Learning Sensitive Patient Intensive Care Unit Mortality

February 14, 2022

FLHub: a Federated Learning model sharing service
Hyunsu Mun, Youngseok Lee
Federated Learning Centralized Machine Learning Deep Learning Library

February 3, 2022

Comparative assessment of federated and centralized machine learning
Ibrahim Abdul Majeed, Sagar Kaushik, Aniruddha Bardhan, Venkata Siva Kumar Tadi, Hwang-Ki Min, Karthikeyan Kumaraguru, Rajasekhara Duvvuru Muni
Gradient Descent Model Learning Comparative Evaluation Centralized Machine Learning Centralized Stochastic Gradient Descent Privacy Advantage

January 7, 2022

Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
Jingwen Zhang, Yuezhou Wu, Rong Pan
Client Contribution Centralized Machine Learning Federated Training Auction Based Horizontal Federated Learning

November 4, 2021

A Personalized Federated Learning Algorithm: an Application in Anomaly Detection
Ali Anaissi, Basem Suleiman
Anomaly Detection Application Proficiency Personalized Federated Learning FedAvg Algorithm Centralized Machine Learning Client Collaboration Optimal Communication