Robust Federated Learning

Robust federated learning (FL) aims to improve the resilience and accuracy of collaborative model training across decentralized devices while preserving data privacy. Current research heavily focuses on mitigating various attacks, including poisoning and gradient leakage, through techniques like secure aggregation, credibility management, and fine-grained learnable masks, often incorporating primal-dual optimization or knowledge distillation methods. These advancements are crucial for enabling trustworthy and reliable FL deployments in sensitive applications like medical diagnostics and network security, where data privacy and model robustness are paramount.

Papers

November 5, 2024

Formal Logic-guided Robust Federated Learning against Poisoning Attacks
Dung Thuy Nguyen, Ziyan An, Taylor T. Johnson, Meiyi Ma, Kevin Leach
Poisoning Attack Adversarial Behavior Adversarial Agent Robust Federated Learning Model Level Defense

October 23, 2024

ProFL: Performative Robust Optimal Federated Learning
Xue Zheng, Tian Xie, Xuwei Tan, Aylin Yener, Xueru Zhang, Ali Payani, Myungjin Lee
Federated Learning Performative Prediction Robust Federated Learning Data Distribution Shift

September 30, 2024

Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition
Ivan Reyes-Amezcua, Michael Rojas-Ruiz, Gilberto Ochoa-Ruiz, Andres Mendez-Vazquez, Christian Daul
Federated Learning Pre Trained Model Robust Federated Learning

May 24, 2024

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation
Peihua Mai, Ran Yan, Yan Pang
Federated Learning Poisoning Attack Secure Aggregation Robust Aggregation Robust Federated Learning Secret Sharing

May 20, 2024

Fed-Credit: Robust Federated Learning with Credibility Management
Jiayan Chen, Zhirong Qian, Tianhui Meng, Xitong Gao, Tian Wang, Weijia Jia
Adversarial Attack Robust Federated Learning Credibility Assessment

December 19, 2023

SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks
Peishen Yan, Hao Wang, Tao Song, Yang Hua, Ruhui Ma, Ningxin Hu, Mohammad R. Haghighat, Haibing Guan
Fine Grained Byzantine Robust Federated Learning Robust Federated Learning Malicious Model

September 22, 2023

Multiply Robust Federated Estimation of Targeted Average Treatment Effects
Larry Han, Zhu Shen, Jose Zubizarreta
Covariate Shift Multi Center Study Average Treatment Effect Robust Federated Learning Multi Site Data

August 31, 2023

Robust Networked Federated Learning for Localization
Reza Mirzaeifard, Naveen K. D. Venkategowda, Stefan Werner
Localization Focus Robust Federated Learning Convergent Algorithm

August 21, 2023

Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap
Shiyuan Zuo, Rongfei Fan, Han Hu, Ning Zhang, Shimin Gong
Byzantine Attack Linear Convergence Robust Aggregation Optimality Gap Robust Federated Learning

July 25, 2023

Scaff-PD: Communication Efficient Fair and Robust Federated Learning
Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
Native Robustness Communication Efficient Communication Efficiency Robust Federated Learning Robust Objective

May 9, 2023

FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Nannan Wu, Li Yu, Xuefeng Jiang, Kwang-Ting Cheng, Zengqiang Yan
Label Noise Class Imbalance Noisy Label Learning Robust Federated Learning

March 23, 2023

AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT
Edward H. Lee, Brendan Kelly, Emre Altinmakas, Hakan Dogan, Maryam Mohammadzadeh, Errol Colak, Steve Fu, Olivia Choudhury, Ujjwal Ratan, Felipe Kitamura, Hernan Chaves, Jimmy Zheng, Mourad Said, Eduardo Reis, Jaekwang Lim, Patricia Yokoo, Courtney Mitchell, Golnaz Houshmand, Marzyeh Ghassemi, Ronan Killeen, Wendy Qiu, Joel Hayden, Farnaz Rafiee, Chad Klochko, Nicholas Bevins, Faeze Sazgara, S. Simon Wong, Michael Moseley, Safwan Halabi, Kristen W. Yeom
Artificial Intelligence Model Data Heterogeneity Robust Federated Learning

November 27, 2022

Federated Learning Attacks and Defenses: A Survey
Yao Chen, Yijie Gui, Hong Lin, Wensheng Gan, Yongdong Wu
Machine Learning Timely Survey Machine Learning Model Federated Learning Robust Federated Learning

October 23, 2022

FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, Xiangyu Zhang
Federated Learning Backdoor Attack Attack Success Rate Robust Federated Learning Negative Flip Provable Defense

October 8, 2022

FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
Jiahui Chen, Yi Zhao, Qi Li, Xuewei Feng, Ke Xu
Adversarial Attack Federated Learning Gradient Leakage Robust Federated Learning Adversarial Traffic

October 6, 2022

Communication-Efficient and Drift-Robust Federated Learning via Elastic Net
Seonhyeong Kim, Jiheon Woo, Daewon Seo, Yongjune Kim
Robust Federated Learning Elastic Net Norm Regularization

June 3, 2022

On the Generalization of Wasserstein Robust Federated Learning
Tung-Anh Nguyen, Tuan Dung Nguyen, Long Tan Le, Canh T. Dinh, Nguyen H. Tran
Strong Generalization Empirical Risk Minimization Robust Optimization Robust Federated Learning Unseen Test Distribution

April 16, 2022

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
Bingzhe Wu, Zhipeng Liang, Yuxuan Han, Yatao Bian, Peilin Zhao, Junzhou Huang
Robust Optimization Data Heterogeneity Robust Federated Learning Learning Probability Distribution Local Mixup Drug Target Affinity Prediction

April 7, 2022

FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity
Yonghai Gong, Yichuan Li, Nikolaos M. Freris
Adaptive Importance Primal Dual Heterogeneous Data Non Convex Objective Robust Federated Learning System Heterogeneity

April 4, 2022

CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
Huy Q. Le, Minh N. H. Nguyen, Shashi Raj Pandey, Chaoning Zhang, Choong Seon Hong
Knowledge Transfer Proxy Dataset Robust Federated Learning Hand Crafted Descriptor

Robust Federated Learning

Papers

Formal Logic-guided Robust Federated Learning against Poisoning Attacks

ProFL: Performative Robust Optimal Federated Learning

Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

Fed-Credit: Robust Federated Learning with Credibility Management

SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks

Multiply Robust Federated Estimation of Targeted Average Treatment Effects

Robust Networked Federated Learning for Localization

Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap

Scaff-PD: Communication Efficient Fair and Robust Federated Learning

FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity

AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT

Federated Learning Attacks and Defenses: A Survey

FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning

FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems

Communication-Efficient and Drift-Robust Federated Learning via Elastic Net

On the Generalization of Wasserstein Robust Federated Learning

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup

FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity

CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning