arXiv research

Paper addresses fault-tolerance in distributed machine learning with stochastic gradient descent.

SignSGD improves distributed learning by tolerating faulty devices, including Byzantine adversaries.

New algorithm improves decentralized learning in the presence of Byzantine faults.

DynBRO learns robustly from dynamic Byzantine workers.

Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfu…

Paper proposes a secure protocol for federated learning.

This paper considers the problem of Byzantine fault tolerance in distributed linear regression in a multi-agent system. However, the proposed algorithms are given for a more general class of distributed optimization problems, of which distributed linear regression is a special case. The system comprises of a server and…

We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles …

Paper tackles Byzantine attacks in Federated Learning by clustering and robustifying.

Paper addresses Byzantine attacks in decentralized optimization over networks.

Paper tackles Byzantine attacks in distributed learning with a new ADMM method.

Fault-tolerant neural networks inspired by biological error correction codes.

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component failures, which can be all encompassed within the spectrum of a …

This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic…

We propose three new robust aggregation rules for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the workers in the parameter server~(PS) architecture. We prove the Byzantine resilie…

We propose a novel robust aggregation rule for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the workers in the parameter server~(PS) architecture. We prove the Byzantine resilience…

Machine Learning (ML) solutions are nowadays distributed and are prone to various types of component failures, which can be encompassed in so-called Byzantine behavior. This paper introduces LiuBei, a Byzantine-resilient ML algorithm that does not trust any individual component in the network (neither workers nor serve…

CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.

This paper reviews quantum machine learning from NISQ to fault tolerance.

Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning. The Byzantine model captures workers that behave arbitrarily, including malicious and compromised workers. In this paper, we break two prevailing Byzantine-tolerant techniques. Specifically we show robu…

Paper develops Byzantine-resilient algorithms for decentralized learning.

Novel algorithm resists Byzantine attacks in federated learning for PCA and LRCS.

Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, corrupt data, or even malicious attacks. We introduce \emph{Karda…

New method improves model accuracy in Byzantine-robust distributed learning by optimizing batch size.

New methods improve Byzantine robustness in distributed learning.

New attack strategy circumvents CC framework's defences in federated learning.

PRISM-FCP improves federated prediction robustness against Byzantine attacks.

Paper improves statistical efficiency of median-of-means estimator for Byzantine robust distributed inference.

The growth of data, the need for scalability and the complexity of models used in modern machine learning calls for distributed implementations. Yet, as of today, distributed machine learning frameworks have largely ignored the possibility of arbitrary (i.e., Byzantine) failures. In this paper, we study the robustness …

Robust algorithm for distributed optimization resistant to Byzantine failures.

Paper develops a robust federated recommendation system against poisoning attacks.

Two novel algorithms improve distributed machine learning in the presence of Byzantine adversaries.

Machine learning has begun to play a central role in many applications. A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints (e.g., multiagent systems) or computational/privacy reasons (e.g., learning on smartp…

COMRADE is a communication-efficient, Byzantine-resilient second-order optimization algorithm.

This paper extends Newton's method to distributed learning, avoiding saddle points and handling Byzantine workers.

New federated learning protocols resist Byzantine failures and offer privacy guarantees.

Paper tackles Byzantine resilience in distributed multi-task learning.

New bucketing scheme improves Byzantine robustness for heterogeneous data.

Paper addresses robust federated linear bandits against Byzantine attacks.

A method to robustly federate learning with non-i.i.d. data and Byzantine workers.

New research shows Byzantine failures hurt generalization more than data poisoning in robust distributed learning.

We analyze the adversarial examples problem in terms of a model's fault tolerance with respect to its input. Whereas previous work focuses on arbitrarily strict threat models, i.e., $ε$-perturbations, we consider arbitrary valid inputs and propose an information-based characteristic for evaluating tolerance to diverse …

Secure federated learning framework resists adversarial users.

Paper shows data poisoning and Byzantine attacks are equivalent, impacting federated learning security.

Proposes a method to improve Byzantine-robustness in compressed federated learning.

To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent approaches have replaced gradient averaging with robust aggregation methods. Such techniques can have high computational costs, often quadratic in the number of compute nodes, and only have limited robustness gua…

New algorithms for collaborative learning in uncertain, decentralized environments.

The loss of a few neurons in a brain rarely results in any visible loss of function. However, the insight into what "few" means in this context is unclear. How many random neuron failures will it take to lead to a visible loss of function? In this paper, we address the fundamental question of the impact of the crash of…