FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication

Most finger vein authentication systems suffer from the problem of small sample size.However,the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity.So the researchers resort to pre-training or multi-source data joint training methods,but these methods will lead to the problem of user privacy leakage.In view of the above issues,this paper proposes a federated learning-based finger vein authentication framework(FedFV)to solve the problem of small sample size and category diversity while protecting user privacy.Through training under FedFV,each client can share the knowledge learned from its user′s finger vein data with the federated client without causing template leaks.In addition,we further propose an efficient personalized federated aggregation algorithm,named federated weighted proportion reduction(FedWPR),to tackle the problem of non-independent identically distribution caused by client diversity,thus achieving the best performance for each client.To thoroughly evaluate the effectiveness of FedFV,comprehensive experiments are conducted on nine publicly available finger vein datasets.Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data.To the best of our knowledge,FedFV is the first personalized federated finger vein authentication framework,which has some reference value for subsequent biometric privacy protection research.

Personalized Federated Learning for Heterogeneous Residential Load Forecasting

Accurate load forecasting is critical for electricity production,transmission,and maintenance.Deep learning(DL)model has replaced other classical models as the most popular prediction models.However,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy risks.Edge nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw data.However,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy disclosure.Accordingly,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these issues.The obtained personalized model outperforms the global model on local data.Further,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy guarantee.Based on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the game.We perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

MpFedcon: Model-Contrastive Personalized Federated Learning with the Class Center

Federated learning is an emerging distributed privacypreserving framework in which parties are trained collaboratively by sharing model or gradient updates instead of sharing private data. However, the heterogeneity of local data distribution poses a significant challenge. This paper focuses on the label distribution skew, where each party can only access a partial set of the whole class set. It makes global updates drift while aggregating these biased local models. In addition, many studies have shown that deep leakage from gradients endangers the reliability of federated learning. To address these challenges, this paper propose a new personalized federated learning method named MpFedcon. It addresses the data heterogeneity problem and privacy leakage problem from global and local perspectives. Our extensive experimental results demonstrate that MpFedcon yields effective resists on the label leakage problem and better performance on various image classification tasks, robust in partial participation settings, non-iid data,and heterogeneous parties.