Federated Transfer Learning

Federated transfer learning (FTL) addresses the challenges of training machine learning models on decentralized, heterogeneous data while preserving privacy. Current research focuses on improving communication efficiency through feature-based approaches, mitigating vulnerabilities to backdoor attacks, and enhancing model adaptability across diverse tasks and domains using techniques like task personalization. FTL's significance lies in its ability to leverage distributed datasets for improved model performance in various applications, including medical diagnosis, industrial IoT security, and manufacturing condition monitoring, while respecting data privacy constraints.

Papers

October 16, 2024

FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
Evelyn Ma, Chao Pan, Rasoul Etesami, Han Zhao, Olgica Milenkovic
Transfer Learning Task Transferability Federated Setting Federated Transfer Learning Transferability Metric Client Variance Reduction

May 15, 2024

Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy
Feng Wang, M. Cenk Gursoy, Senem Velipasalar
Native Robustness Transfer Learning Federated Learning Privacy Policy Communication Efficiency Federated Transfer Learning Information Leakage

April 30, 2024

Let's Focus: Focused Backdoor Attack against Federated Transfer Learning
Marco Arazzi, Stefanos Koffas, Antonino Nocera, Stjepan Picek
Federated Learning Backdoor Attack Federated Transfer Learning Effective Backdoor

April 20, 2024

Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding
Ahmadreza Eslaminia, Yuquan Meng, Klara Nahrstedt, Chenhui Shao
Surface Defect Detection Industrial Application Condition Monitoring Federated Transfer Learning Tool Wear Personalization Task

March 17, 2024

Federated Transfer Learning with Differential Privacy
Mengchu Li, Ye Tian, Yang Feng, Yi Yu
Differential Privacy Data Heterogeneity Privacy Constraint Federated Transfer Learning

March 3, 2024

A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
Wei Guo, Fuzhen Zhuang, Xiao Zhang, Yiqi Tong, Jin Dong
Transfer Learning Technical Challenge Financial Application NCD Method Comprehensive Survey Data Scarcity Data Sharing Incremental Data Federated Transfer Learning

December 29, 2023

FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
Jie Shen, Shusen Yang, Cong Zhao, Xuebin Ren, Peng Zhao, Yuqian Yang, Qing Han, Shuaijun Wu
Vertical Federated Learning Label Free Fault Diagnosis Federated Transfer Learning Intelligent Fault Diagnosis

November 29, 2023

Grounding Foundation Models through Federated Transfer Learning: A General Framework
Yan Kang, Tao Fan, Hanlin Gu, Xiaojin Zhang, Lixin Fan, Qiang Yang
Transfer Learning Foundation Model General Framework Federated Transfer Learning

November 13, 2023

FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition
Egemen İşgüder, Özlem Durmaz İncel
Transfer Learning Sensor Data Sensor Based Human Activity Recognition Device Identification Federated Transfer Learning

October 11, 2023

Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection
Lochana Telugu Rajesh, Tapadhir Das, Raj Mani Shukla, Shamik Sengupta
Network Intrusion Detection Industrial Internet of Thing Internet of Thing Data Federated Transfer Learning IoT Intrusion Detection

September 8, 2023

Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing
Xuwei Yang, Anastasis Kratsios, Florian Krach, Matheus Grasselli, Aurelien Lucchi
Financial Application Kernel Regression Option Pricing Federated Transfer Learning Training Pair Regret Optimal Algorithm

November 20, 2022

Federated deep transfer learning for EEG decoding using multiple BCI tasks
Xiaoxi Wei, A. Aldo Faisal
Federated Prompt Cooperation Deep Transfer Federated Transfer Learning

November 9, 2022

Framework Construction of an Adversarial Federated Transfer Learning Classifier
Hang Yi, Tongxuan Bie, Tongjiang Yan
Generative Adversarial Network Transfer Learning New Framework Image Classification Federated Transfer Learning Patient Privacy

September 12, 2022

Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning
Feng Wang, M. Cenk Gursoy, Senem Velipasalar
Transfer Learning Communication Efficiency Shuffle Model Federated Transfer Learning Learning Scheme

September 5, 2022

Federated Transfer Learning with Multimodal Data
Yulian Sun
Multimodal Data Federated Transfer Learning

July 26, 2022

CFLIT: Coexisting Federated Learning and Information Transfer
Zehong Lin, Hang Liu, Ying-Jun Angela Zhang
Resource Allocation Mobile Edge Information Transfer Federated Transfer Learning Computation Communication

May 31, 2022

A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
Gaode Chen, Yijun Su, Xinghua Zhang, Anmin Hu, Guochun Chen, Siyuan Feng, Ji Xiang, Junbo Zhang, Yu Zheng
Case Study Privacy Issue Federated Transfer Learning Urban Analytics Urban Region Profiling

March 24, 2022

A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study
Alexandros Shikun Zhang, Naomi Fengqi Li
Deep Learning Medical Image Medical Image Classification Limited Data COVID 19 Diagnosis COVID 19 Case Federated Transfer Learning

Federated Transfer Learning

Papers

FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning

Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy

Let's Focus: Focused Backdoor Attack against Federated Transfer Learning

Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding

Federated Transfer Learning with Differential Privacy

A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications

FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning

Grounding Foundation Models through Federated Transfer Learning: A General Framework

FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition

Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection

Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing

Federated deep transfer learning for EEG decoding using multiple BCI tasks

Framework Construction of an Adversarial Federated Transfer Learning Classifier

Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning

Federated Transfer Learning with Multimodal Data

CFLIT: Coexisting Federated Learning and Information Transfer

A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling

A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study