Federated Learning (FL) is a distributed machine learning paradigm that allows clients to
train models on their data while preserving their privacy. FL algorithms, such as Federated …

Federated Learning (FL) is a distributed machine learning paradigm that allows clients to
train models on their data while preserving their privacy. FL algorithms, such as Federated …

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 …

As a mechanism for devices to update a global model without sharing data, federated
learning bridges the tension between the need for data and respect for privacy. However …

Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning
(FL) optimization due to its simple implementation, stateless nature, and privacy guarantees …

Federated learning (FL) coordinates with numerous heterogeneous devices to
collaboratively train a shared model while preserving user privacy. Despite its multiple …

Federated learning (FL) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

Federated Learning (FL) is a decentralized machine learning protocol that allows a set of
participating agents to collaboratively train a model without sharing their data. This makes …

Federated learning (FL) is a distributed learning technique that trains a shared model over
distributed data in a privacy-preserving manner. Unfortunately, FL's performance degrades …

Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data
on edge devices. However, optimizing FL in practice can be difficult due to the diversity and …