arXiv research

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

LiuBei is a resilient ML algorithm that tolerates Byzantine workers and servers without trusting any component.

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 …

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

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 …

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…

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

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

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…

DynBRO learns robustly from dynamic Byzantine workers.

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…

Efficient distributed learning with Byzantine-resilient thresholding and error feedback.

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…

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

Paper proposes a secure protocol for federated learning.

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

Secure federated learning framework resists adversarial users.

DFMR improves robustness of learning finite mixture models in distributed settings.

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 abnormal or even arbitrary and adversarial behavior. In this setting, the Byzantine machines may create fake local minima near a saddle point tha…

We consider the problem of distributed statistical machine learning in adversarial settings, where some unknown and time-varying subset of working machines may be compromised and behave arbitrarily to prevent an accurate model from being learned. This setting captures the potential adversarial attacks faced by Federate…

The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources. As a promising tool to efficiently extract useful information from such big data, machine learning has been pushed to the forefront and seen great success in a wi…

A new method balances model quality and Byzantine robustness in Federated Learning.

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

New bucketing scheme improves Byzantine robustness for heterogeneous data.

Paper develops a robust PP distributed quasi-Newton estimation for Byzantine machines.

Unified framework for Byzantine robust gossip algorithms with guaranteed performance.

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…

This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of the $m$ machines which allegedly compute stochastic gradients every iteration, an $α$-fraction are Byzantine, and can behave arbitrarily and adversarially. Our main result is a variant of stochastic gradient de…

Could a gradient aggregation rule (GAR) for distributed machine learning be both robust and fast? This paper answers by the affirmative through multi-Bulyan. Given $n$ workers, $f$ of which are arbitrary malicious (Byzantine) and $m=n-f$ are not, we prove that multi-Bulyan can ensure a strong form of Byzantine resilien…

Paper addresses Byzantine attacks in decentralized optimization over networks.

We study a recently proposed large-scale distributed learning paradigm, namely Federated Learning, where the worker machines are end users' own devices. Statistical and computational challenges arise in Federated Learning particularly in the presence of heterogeneous data distribution (i.e., data points on different de…

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

Byrd-SAGA reduces variance to robustify SGD against Byzantine attacks.

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…

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

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

Paper develops Byzantine-resilient algorithms for decentralized learning.

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

While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending poisoned gradients …

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

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

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.

Robust algorithm for distributed optimization resistant to Byzantine failures.

Paper develops a robust federated recommendation system against poisoning attacks.

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