Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Federated learning enables a large amount of edge computing devices to jointly learn a
model without data sharing. As a leading algorithm in this setting, Federated Averaging …

Many large-scale machine learning (ML) applications need to perform decentralized
learning over datasets generated at different devices and locations. Such datasets pose a …

Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …

Federated learning enables training on a massive number of edge devices. To improve
flexibility and scalability, we propose a new asynchronous federated optimization algorithm …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Federated averaging (FedAvg) is a communication-efficient algorithm for distributed training
with an enormous number of clients. In FedAvg, clients keep their data locally for privacy …