Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated Learning (FL) is a paradigm that permits to learn a Deep Learning model without
centralizing raw data, and has recently received growing interest primarily as a solution to …

Federated Learning (FL) is a privacy-oriented framework that allows distributed edge
devices to jointly train a shared global model without transmitting their sensed data to …

Federated Learning (FL) is a promising paradigm widely used in privacy-preserving
machine learning. It enables distributed devices to collaboratively train a model while …

Federated learning (FL) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with
enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …

Federated learning is a widely used distributed deep learning framework that protects the
privacy of each client by exchanging model parameters rather than raw data. However …

In recent years, federated learning (FL) plays an important role in data privacy-sensitive
scenarios to perform learning works collectively without data exchange. However, due to the …