Communication overhead has become one of the major bottlenecks in the distributed
training of modern deep neural networks. With such consideration, various quantization …

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 …

In practical federated learning (FL) systems, the presence of malicious Byzantine attacks
and data heterogeneity often introduces biases into the learning process. However, existing …

Decentralized optimization has become a standard paradigm for solving large-scale
decision-making problems and training large machine learning models without centralizing …

The emerging federated learning applications raise challenges of Byzantine-robustness and
communication efficiency in distributed non-convex learning over non-IID data. To address …

This paper deals with federated learning (FL) in the presence of malicious Byzantine attacks
and data heterogeneity. A novel Robust Average Gradient Algorithm (RAGA) is proposed …

Distributed learning has become the standard approach for training large-scale machine
learning models across private data silos. While distributed learning enhances privacy …

Federated learning involves a group of workers and a central server to train a machine
learning model in a distributed manner. However, the distributed structure poses challenges …