A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
SignSGD improves distributed learning by tolerating faulty devices, including Byzantine adversaries.
Paper tackles Byzantine attacks in Federated Learning by clustering and robustifying.
Two novel algorithms improve distributed machine learning in the presence of Byzantine adversaries.
New algorithm identifies near-optimal policies in adversarial distributed RL settings.
Dynamic defense against Byzantine poisoning in federated learning.
PRISM-FCP improves federated prediction robustness against Byzantine attacks.
This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of the machines which allegedly compute stochastic gradients every iteration, an -fraction are Byzantine, and can behave arbitrarily and adversarially. Our main result is a variant of stochastic gradient de…
Secure federated learning framework resists adversarial users.
Robust algorithm for distributed optimization resistant to Byzantine failures.
Paper improves statistical efficiency of median-of-means estimator for Byzantine robust distributed inference.
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…
Unified framework for Byzantine robust gossip algorithms with guaranteed performance.
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 …
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…
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework. In r…
New algorithms for collaborative learning in uncertain, decentralized environments.
This work proves MultiKrum is robust in mean estimation with adversaries.
Byzantine-resilient federated learning with local iterations and robust mean estimation.
Paper develops efficient algorithms for robust distributed learning with statistical guarantees.
In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required comm…
Distributed model training is vulnerable to byzantine system failures and adversarial compute nodes, i.e., nodes that use malicious updates to corrupt the global model stored at a parameter server (PS). To guarantee some form of robustness, recent work suggests using variants of the geometric median as an aggregation r…
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…
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…
Paper addresses Byzantine attacks in decentralized optimization over networks.
Paper tackles Byzantine attacks in distributed learning with a new ADMM method.
Byrd-SAGA reduces variance to robustify SGD against Byzantine attacks.
DynBRO learns robustly from dynamic Byzantine workers.
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 …
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.
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…
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.
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…
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 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.
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 …
Paper develops a robust federated recommendation system against poisoning attacks.
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.
In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithm…
This paper extends Newton's method to distributed learning, avoiding saddle points and handling Byzantine workers.