Training large machine learning models requires a distributed computing approach, with
communication of the model updates being the bottleneck. For this reason, several methods …

Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in
collaborative and federated learning. However, many fruitful directions, such as the usage of …

In recent years, deep learning methods based on a large amount of data have achieved
substantial success in numerous fields. However, with increases in regulations for protecting …

Byzantine robustness is an essential feature of algorithms for certain distributed optimization
problems, typically encountered in collaborative/federated learning. These problems are …

Distributed learning has emerged as a leading paradigm for training large machine learning
models. However, in real-world scenarios, participants may be unreliable or malicious …

We present a partially personalized formulation of Federated Learning (FL) that strikes a
balance between the flexibility of personalization and cooperativeness of global training. In …

Robustness to Byzantine attacks is a necessity for various distributed training scenarios.
When the training reduces to the process of solving a minimization problem, Byzantine …

Communication between workers and the master node to collect local stochastic gradients is
a key bottleneck in a large-scale distributed learning system. Various recent works have …

We develop a communication-efficient distributed learning algorithm that is robust against
Byzantine worker machines. We propose and analyze a distributed gradient-descent …

We develop a communication-efficient distributed learning algorithm that is robust against
Byzantine worker machines. We propose and analyze a distributed gradient-descent …