Federated learning is a key scenario in modern large-scale machine learning where the
data remains distributed over a large number of clients and the task is to learn a centralized …

We propose a novel framework to study asynchronous federated learning optimization with
delays in gradient updates. Our theoretical framework extends the standard FedAvg …

Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of
the data across different clients which gives rise to the client drift phenomenon. In fact …

Federated Learning (FL) is popular for communication-efficient learning from distributed
data. To utilize data at different clients without moving them to the cloud, algorithms such as …

Federated Learning is a distributed learning setting where the goal is to train a centralized
model with training data distributed over a large number of heterogeneous clients, each with …

Federated learning is an approach to distributed machine learning where a global model is
learned by aggregating models that have been trained locally on data-generating clients …

A key learning scenario in large-scale applications is that of federated learning, where a
centralized model is trained based on data originating from a large number of clients. We …

Federated learning is a distributed machine learning paradigm in which a large number of
clients coordinate with a central server to learn a model without sharing their own training …

It is well understood that client-master communication can be a primary bottleneck in
Federated Learning. In this work, we address this issue with a novel client subsampling …

The federated learning (FL) framework trains a machine learning model using decentralized
data stored at edge client devices by periodically aggregating locally trained models …