Decentralized optimization methods enable on-device training of machine learning models
without a central coordinator. In many scenarios communication between devices is energy …

With huge amounts of training data, deep learning has made great breakthroughs in many
artificial intelligence (AI) applications. However, such large-scale data sets present …

In this paper, we study the problem of distributed optimization using an arbitrary network of
lightweight computing nodes, where each node can only send/receive information to/from its …

Due to the additive property of most machine learning objective functions, the training can
be distributed to multiple machines. Distributed machine learning is an efficient way to deal …

Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on
massive devices simultaneously without the control of a central server. However, existing …

When scaling distributed training, the communication overhead is often the bottleneck. In
this paper, we propose a novel SGD variant with reduced communication and adaptive …

Communication is widely known as the primary bottleneck of federated learning, and
quantization of local model updates before uploading to the parameter server is an effective …

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 …

We consider decentralized model training in tiered communication networks. Our network
model consists of a set of silos, each holding a vertical partition of the data. Each silo …

Decentralized federated learning, inherited from decentralized learning, enables the edge
devices to collaborate on model training in a peer-to-peer manner without the assistance of …