Publications de Nirupam Gupta | EPFL Graph Search

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Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

This paper considers the multi-agent linear least-squares problem in a server-agent network architecture. The system comprises multiple agents, each with a set of local data points. The agents are connected to a server, and there is no inter-agent communic ...

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called workers). Beyond accuracy, the u ...

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

Large Language Models (LLMs) have gained significant attention in recent years due to their potential to revolutionize various industries and sectors. However, scaling LLMs further requires access to substantial linguistic resources that are being rapidly ...

This paper considers the problem of Byzantine fault-tolerance in distributed multi-agent optimization. In this problem, each agent has a local cost function, and in the fault-free case, the goal is to design a distributed algorithm that allows all the agen ...

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

This article considers solving an overdetermined system of linear equations in peer-to-peer multiagent networks. The network is assumed to be synchronous and strongly connected. Each agent has a set of local data points, and their goal is to compute a line ...

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

f

Byzantine-faulty agents, which may not follow a prescr ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learning. In this problem, each agent samples data points independently from a ce ...

The problem of Byzantine resilience in distributed machine learning, a.k.a., Byzantine machine learning, consists in designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes, i.e., nodes with corrupt data or ...

Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines. Although this problem received significant attention, prior works often assume the data held by the machines to be homo ...

Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may mis ...

This paper considers the problem of exact Byzantine fault-tolerance in multi-agent decentralized optimization. We consider a complete peer-to-peer network of n agents; each agent has a local cost function, however, up to f of the agents are Byzantine fault ...

Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades the iter ...

Federated learning (FL) is an appealing paradigm that allows a group of machines (a.k.a. clients) to learn collectively while keeping their data local. However, due to the heterogeneity between the clients' data distributions, the model obtained through th ...

This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible with both diff ...

Nirupam Gupta

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Robust Collaborative Learning with Linear Gradient Overhead

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Democratizing Machine Learning

Brief Announcement: A Case for Byzantine Machine Learning

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Robust and Private Federated Learning on LLMs

Approximate Byzantine Fault-Tolerance in Distributed Optimization

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Byzantine Machine Learning: A Primer

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Robust Machine Learning - Distributed Methods for Safe AI

Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy

Revisiting Ensembling in One-Shot Federated Learning

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?