$\alpha$-Weighted Federated Adversarial Training
Federated Adversarial Training (FAT) helps us address the data privacy and governance issues, meanwhile maintains the model robustness to the adversarial attack. However, the inner-maximization optimization of Adversarial Training can exacerbate the data heterogeneity among local clients, which triggers the pain points of Federated Learning. This makes that the straightforward combination of two paradigms shows the performance deterioration as observed in previous works. In this paper, we introduce an $\alpha$-Weighted Federated Adversarial Training ($\alpha$-WFAT) method to overcome this problem, which relaxes the inner-maximization of Adversarial Training into a lower bound friendly to Federated Learning. We present the theoretical analysis about this $\alpha$-weighted mechanism and its effect on the convergence of FAT. Empirically, the extensive experiments are conducted to comprehensively understand the characteristics of $\alpha$-WFAT, and the results on three benchmark datasets demonstrate $\alpha$-WFAT significantly outperforms FAT under different adversarial learning methods and federated optimization methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackFederated LearningSimilar Papers 제목 키워드 기반
When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity
Federated learning (FL) is increasingly used to fine-tune foundation models (FMs) on distributed private data. The community largely assumes that large-scale pretraining serves as a 'rising tide that lifts all boats' in …
Text ClassificationFederated LearningUsing adversarial images to improve outcomes of federated learning for non-IID data
One of the important problems in federated learning is how to deal with unbalanced data. This contribution introduces a novel technique designed to deal with label skewed non-IID data, using adversarial inputs, created b…
Federated Learningimage-classificationImage ClassificationFederated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks
Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data to the edge server. However, data heter…
Data AugmentationFederated LearningFed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning
Federated learning (FL) enables collaborative model training without sharing raw patient data, but standard approaches such as FedAvg treat each client as a black box and provide no mechanism for isolating an adversarial…
Federated LearningHybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Federated learning has emerged as a promising approach for collaborative and privacy-preserving learning. Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of…
Federated LearningPrivacy Preserving