Distributed gradient descent algorithm robust to an arbitrary number of byzantine attackers
X Cao, L Lai - IEEE Transactions on Signal Processing, 2019 - ieeexplore.ieee.org
Due to the growth of modern dataset size and the desire to harness computing power of
multiple machines, there is a recent surge of interest in the design of distributed machine …
multiple machines, there is a recent surge of interest in the design of distributed machine …
Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach
In this work, we consider the resilience of distributed algorithms based on stochastic
gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who …
gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who …
Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks
This paper deals with distributed finite-sum optimization for learning over multiple workers in
the presence of malicious Byzantine attacks. Most resilient approaches so far combine …
the presence of malicious Byzantine attacks. Most resilient approaches so far combine …
ByGARS: Byzantine SGD with arbitrary number of attackers
We propose two novel stochastic gradient descent algorithms, ByGARS and ByGARS++, for
distributed machine learning in the presence of any number of Byzantine adversaries. In …
distributed machine learning in the presence of any number of Byzantine adversaries. In …
Distributed training with heterogeneous data: Bridging median-and mean-based algorithms
Recently, there is a growing interest in the study of median-based algorithms for distributed
non-convex optimization. Two prominent examples include signSGD with majority vote, an …
non-convex optimization. Two prominent examples include signSGD with majority vote, an …
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
We consider the distributed statistical learning problem over decentralized systems that are
prone to adversarial attacks. This setup arises in many practical applications, including …
prone to adversarial attacks. This setup arises in many practical applications, including …
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
We consider the distributed statistical learning problem over decentralized systems that are
prone to adversarial attacks. This setup arises in many practical applications, including …
prone to adversarial attacks. This setup arises in many practical applications, including …
FABA: an algorithm for fast aggregation against byzantine attacks in distributed neural networks
Many times, training a large scale deep learning neural network on a single machine
becomes more and more difficult for a complex network model. Distributed training provides …
becomes more and more difficult for a complex network model. Distributed training provides …
Byzantine-robust distributed learning: Towards optimal statistical rates
In this paper, we develop distributed optimization algorithms that are provably robust against
Byzantine failures—arbitrary and potentially adversarial behavior, in distributed computing …
Byzantine failures—arbitrary and potentially adversarial behavior, in distributed computing …
Defending against saddle point attack in Byzantine-robust distributed learning
We study robust distributed learning that involves minimizing a non-convex loss function
with saddle points. We consider the Byzantine setting where some worker machines have …
with saddle points. We consider the Byzantine setting where some worker machines have …