Add to ORKGFederated learning (FL) learns a model jointly from a set of participating devices without sharing each other's privately held data. The characteristics of non-i.i.d. data across the network, low device participation, high communication costs, and the mandate that data remain private bring challenges in understanding the convergence of FL algorithms, particularly with regards to how convergence scales with the number of participating devices. In this paper, we focus on Federated Averaging (FedAvg)--arguably the most popular and effective FL algorithm class in use today--and provide a unified and comprehensive study of its convergence rate. Although FedAvg has recently been studied by an emerging line of literature, a systematic study of how...
One of the crucial issues in federated learning is how to develop efficient optimization algorithms....
Federated learning (FL) has become de facto framework for collaborative learning among edge devices ...
A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in ...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated learning enables a large amount of edge computing devices to learn a model without data sh...
Federated Averaging (FEDAVG) has emerged as the algorithm of choice for federated learning due to it...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model on a mas...
Federated learning (FL) faces challenges of intermittent client availability and computation/communi...
Federated learning (FL) is a fast-developing technique that allows multiple workers to train a globa...
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous c...
We propose a solution to address the lack of high-probability guarantees in Federated Learning (FL) ...
Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning mode...
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inferen...
We present a federated learning framework that is designed to robustly deliver good predictive perfo...
One of the crucial issues in federated learning is how to develop efficient optimization algorithms....
Federated learning (FL) has become de facto framework for collaborative learning among edge devices ...
A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in ...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated learning enables a large amount of edge computing devices to learn a model without data sh...
Federated Averaging (FEDAVG) has emerged as the algorithm of choice for federated learning due to it...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model on a mas...
Federated learning (FL) faces challenges of intermittent client availability and computation/communi...
Federated learning (FL) is a fast-developing technique that allows multiple workers to train a globa...
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous c...
We propose a solution to address the lack of high-probability guarantees in Federated Learning (FL) ...
Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning mode...
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inferen...
We present a federated learning framework that is designed to robustly deliver good predictive perfo...
One of the crucial issues in federated learning is how to develop efficient optimization algorithms....
Federated learning (FL) has become de facto framework for collaborative learning among edge devices ...
A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in ...