Federated Image Classification

Federated image classification focuses on training accurate image classification models collaboratively across multiple decentralized devices without directly sharing sensitive data. Current research emphasizes improving efficiency and robustness, exploring various weight aggregation strategies, and addressing challenges like non-independent and identically distributed (non-IID) data and communication overhead. This field is significant for enabling privacy-preserving machine learning in diverse applications, such as healthcare and manufacturing, where data is often siloed and sensitive. Active research areas include developing novel aggregation algorithms, adapting existing architectures (e.g., EfficientNet, ResNet, SVMs) to the federated setting, and enhancing model interpretability.

Papers

August 19, 2024

Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing
Vinit Hegiste, Tatjana Legler, Martin Ruskowski
Empirical Study Manufacturing Industry EfficientNet Algorithm Federated Image Classification Weight Selection Epoch Based

August 12, 2024

Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability
Mario Padilla Rodriguez, Mohamed Nafea
Shapley Additive Explanation Cardiovascular Disease Risk Centralized Control Federated Machine Learning Federated Image Classification

July 17, 2024

CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering
Ensieh Khazaei, Alireza Esmaeilzehi, Bilal Taha, Dimitrios Hatzinakos
Contrastive Learning Activity Recognition Sensor Data Deep Clustering Privacy Preserving Image Federated Image Classification Federated Learning Client

July 11, 2024

FedLog: Personalized Federated Classification with Less Communication and More Flexibility
Haolin Yu, Guojun Zhang, Pascal Poupart
Differential Privacy Large Scale Private Data New Algorithm Federated Image Classification Limited Communication

May 24, 2024

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
Akash Dhasade, Anne-Marie Kermarrec, Tuan-Anh Nguyen, Rafael Pires, Martijn de Vos
Federated Learning Wire Harness Federated Image Classification Expert Delineated Cohort

January 26, 2024

FedGT: Federated Node Classification with Scalable Graph Transformer
Zaixi Zhang, Qingyong Hu, Yang Yu, Weibo Gao, Qi Liu
Graph Neural Network Subgraph Structure Local Graph Federated Image Classification Scalable Graph Transformer

January 22, 2024

January 3, 2024

Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
Shengchao Chen, Ting Shu, Huan Zhao, Jiahao Wang, Sufen Ren, Lina Yang
Fine Grained Parameter Efficient Free Lunch Fine Grained Image Classification High Resolution Remote Sensing Federated Image Classification

November 24, 2023

ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni
Supervised ImageNet New Initialization Architecture Perspective Normalization Layer Federated Image Classification Medical Image Classification Task Diva 360

September 21, 2023

Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications
Yusen Wu, Jamie Deng, Hao Chen, Phuong Nguyen, Yelena Yesha
Baseline Result Federated Image Classification Structural Outlier Aggregated Gradient Mean Aggregation Decentralized Deep Learning

August 14, 2023

Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
Saurav Prakash, Jin Sima, Chao Pan, Eli Chien, Olgica Milenkovic
Secure Aggregation Hyperbolic Space Convex Hull Euclidean Space Federated Image Classification Convex Machine Learning

May 1, 2023

FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring
Jeffry Wicaksana, Zengqiang Yan, Kwang-Ting Cheng
Federated Image Classification Intracranial Hemorrhage Weight Anchoring

October 28, 2022

Distributed Black-box Attack against Image Classification Cloud Services
Han Wu, Sareh Rowlands, Johan Wahlstrom
Adversarial Attack Black Box Attack Success Rate Black Box Attack Federated Image Classification Misclassified Image

August 26, 2022

Abnormal Local Clustering in Federated Learning
Jihwan Won
Private Data Local Model Federated Image Classification Evident Abnormality Local Clustering Minimum Sum of Square Clustering

June 27, 2022

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Meirui Jiang, Hongzheng Yang, Xiaoxiao Li, Quande Liu, Pheng-Ann Heng, Qi Dou
Semi Supervised Class Imbalance Medical Datasets Dynamic Learning Federated Image Classification

June 17, 2022

FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E Turner
Transfer Learning Parameter Efficient Personalized Subject Federated Image Classification Modern Deep Learning Better FiT Shot Transfer Learning

June 3, 2022

Supernet Training for Federated Image Classification under System Heterogeneity
Taehyeon Kim, Se-Young Yun
Supernet Training Weight Sharing Federated Image Classification System Heterogeneity Heterogeneous Training

April 22, 2022

A Closer Look at Personalization in Federated Image Classification
Changxing Jing, Yan Huang, Yihong Zhuang, Liyan Sun, Yue Huang, Zhenlong Xiao, Xinghao Ding
Representation Learning Glance Annotation Joint Representation Federated Image Classification

December 14, 2021

Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement
Parikshit Ram, Kaushik Sinha
Federated Learning Federated Image Classification Hub Structure Nearest Neighbor Classifier

Federated Image Classification

Papers

Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing

Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability

CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering

FedLog: Personalized Federated Classification with Less Communication and More Flexibility

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

FedGT: Federated Node Classification with Scalable Graph Transformer

TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients

AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework

ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification

Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications

Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls

FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring

Distributed Black-box Attack against Image Classification Cloud Services

Abnormal Local Clustering in Federated Learning

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification

Supernet Training for Federated Image Classification under System Heterogeneity

A Closer Look at Personalization in Federated Image Classification

Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement