We consider distributed optimization over several devices, each sending incremental model
updates to a central server. This setting is considered, for instance, in federated learning …

Due to the explosion in the size of the training datasets, distributed learning has received
growing interest in recent years. One of the major bottlenecks is the large communication …

Lossy gradient compression, with either unbiased or biased compressors, has become a
key tool to avoid the communication bottleneck in centrally coordinated distributed training of …

We consider the problem of distributed mean estimation (DME), in which $ n $ machines are
each given a local $ d $-dimensional vector $ x_v\in\mathbb {R}^ d $, and must cooperate to …

We consider a standard distributed optimisation setting where $ N $ machines, each holding
a $ d $-dimensional function $ f_i $, aim to jointly minimise the sum of the functions $\sum …

Federated learning (FL) is an emerging machine learning paradigm involving multiple
clients, eg, mobile phone devices, with an incentive to collaborate in solving a machine …

Motivated by the interest in communication-efficient methods for distributed machine
learning, we consider the communication complexity of minimising a sum of $ d …

Aquatic primary productivity mainly depends on pelagic phytoplankton. The globally
abundant marine picocyanobacteria Prochlorococcus comprises a significant fraction of the …