As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

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

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Federated learning (FL) allows model training from local data collected by edge/mobile
devices while preserving data privacy, which has wide applicability to image and vision …

Federated learning (FL) is an emerging technique for training machine learning models
using geographically dispersed data collected by local entities. It includes local computation …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

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 …

Scalable training of large models (like BERT and GPT-3) requires careful optimization
rooted in model design, architecture, and system capabilities. From a system standpoint …

The advances in today's IoT devices and machine learning methods have given rise to the
concept of Federated Learning. Through such a technique, a plethora of network devices …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …