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

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

The present paper develops a novel aggregated gradient approach for distributed machine
learning that adaptively compresses the gradient communication. The key idea is to first …

In distributed optimization and machine learning, multiple nodes coordinate to solve large
problems. To do this, the nodes need to compress important algorithm information to bits so …

When gradient descent (GD) is scaled to many parallel workers for large-scale machine
learning applications, its per-iteration computation time is limited by straggling workers …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

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 …

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Distributed learning has become a critical enabler of the massively connected world that
many people envision. This article discusses four key elements of scalable distributed …