Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

Federated Learning (FL) has recently emerged as a promising distributed machine learning
framework to preserve clients' privacy, by allowing multiple clients to upload the gradients …

Federated Learning (FL) has emerged as a powerful paradigm for training Machine
Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while …

Secure aggregation promises a heightened level of privacy in federated learning,
maintaining that a server only has access to a decrypted aggregate update. Within this …

Federated learning has been proposed as a privacy-preserving machine learning
framework that enables multiple clients to collaborate without sharing raw data. However …

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to
evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed …

Federated learning is a popular distributed machine learning paradigm that enables
collaborative model training at multiple entities via exchanging intermediate learning results …

Cross-silo federated learning (FL) is promising in facilitating data collaboration across
various organizations, which greatly alleviates the information silo problem in industries and …

Gradient inversion attacks on federated learning systems reconstruct client training data
from exchanged gradient information. To defend against such attacks, a variety of defense …

Federated learning (FL) enables collaborative model training among multiple clients without
raw data exposure. However, recent studies have shown that clients' private training data …