Federated Learning is a machine learning setting where the goal is to train a high-quality
centralized model while training data remains distributed over a large number of clients each …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile devices
or whole organizations) collaboratively train a model under the orchestration of a central …

Motivated by the need for distributed learning and optimization algorithms with low
communication cost, we study communication efficient algorithms for distributed mean estimation. …

Communication cost is often a bottleneck in federated learning and other client-based
distributed learning scenarios. To overcome this, several gradient compression and model …

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 …

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 argue …

Federated learning and analytics are a distributed approach for collaboratively learning
models (or statistics) from decentralized data, motivated by and designed for privacy protection. …

The standard objective in machine learning is to train a single model for all users. However,
in many learning scenarios, such as cloud computing and federated learning, it is possible …

The decentralized nature of federated learning makes detecting and defending against
adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated …

Distributed stochastic gradient descent is an important subroutine in distributed learning. A
setting of particular interest is when the clients are mobile devices, where two important …