Federated learning involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while kee** data localized. Training in …

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 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 …

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 …

Federated Learning (FL) is known to perform Machine Learning tasks in a distributed
manner. Over the years, this has become an emerging technology especially with various …

Decentralised learning is attracting more and more interest because it embodies the
principles of data minimisation and focused data collection, while favouring the transparency …

We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …

In this paper, we develop distributed optimization algorithms that are provably robust against
Byzantine failures—arbitrary and potentially adversarial behavior, in distributed computing …

We propose a new optimization formulation for training federated learning models. The
standard formulation has the form of an empirical risk minimization problem constructed to …

Federated learning obtains a central model on the server by aggregating models trained
locally on clients. As a result, federated learning does not require clients to upload their data …