Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Federated learning (FL) enables distributed joint training of machine learning (ML) models
without the need to share local data. FL is, however, not immune to privacy threats such as …

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

Federated learning (FL) is a technique that enables clients to collaboratively train a model
by sharing local models instead of raw private data. However, existing reconstruction attacks …

Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to
collaborate on building a machine learning model without sharing their private data …

We consider a federated learning (FL) setting where a machine learning model with a fully
connected first layer is trained between different clients and a central server using FedAvg …

Federated learning (FL) emerged as a paradigm designed to improve data privacy by
enabling data to reside at its source, thus embedding privacy as a core consideration in FL …

Federated Learning has emerged as a leading approach for decentralized machine
learning, enabling multiple clients to collaboratively train a shared model without …

The growing use of deep learning models in end-device applications has led to various
inference attacks and associated data privacy threats. Recent research also reveals the …

Federated learning (FL) is widely studied for local privacy protection, and it involves
exchanging model parameters rather than raw data among clients. However, gradient …