Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Federated learning is a distributed optimization paradigm that enables a large number of
resource-limited client nodes to cooperatively train a model without data sharing. Previous …

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

Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …

We introduce ProxSkip—a surprisingly simple and provably efficient method for minimizing
the sum of a smooth ($ f $) and an expensive nonsmooth proximable ($\psi $) function. The …

In federated learning, heterogeneity in the clients' local datasets and computation speeds
results in large variations in the number of local updates performed by each client in each …

Federated learning is a distributed optimization paradigm that enables a large number of
resource-limited client nodes to cooperatively train a model without data sharing. Several …

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 …

Investigation of the degree of personalization in federated learning algorithms has shown
that only maximizing the performance of the global model will confine the capacity of the …

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …