This paper considers the problem of distributed learning (DL) in the presence of stragglers.
For this problem, DL methods based on gradient coding have been widely investigated …

To fully exploit enormous data generated by intelligent devices in edge computing, edge
federated learning (EFL) is envisioned as a promising solution. The distributed collaborative …

In the era of artificial intelligence and big data, the demand for data processing has surged,
leading to larger datasets and computation capability. Distributed machine learning (DML) …

Federated learning (FL), particularly when data is distributed across multiple clients, helps
reducing the learning time by avoiding training on a massive pile-up of data. Nonetheless …

Distributed implementations are crucial in speeding up large scale machine learning
applications. Distributed gradient descent (GD) is widely employed to parallelize the …

Machine learning (ML) has been widely applied to successfully address a variety of different
problems across diverse domains, such as robotics, healthcare, and finance. However, high …

Gradient coding is a method for mitigating straggling servers in a centralized computing
network that uses erasure-coding techniques to distributively carry out first-order …

The Industrial Metaverse is driving a new revolution wave for smart manufacturing domain
by reproducing the real industrial environment in a virtual space. Real-time synchronization …

Edge computing has recently emerged as a promising paradigm to boost the performance of
distributed learning by leveraging the distributed resources at edge nodes. Architecturally …

Within distributed learning, workers typically compute gradients on their assigned dataset
chunks and send them to the parameter server (PS), which aggregates them to compute …