Genuinely Distributed Byzantine Machine Learning

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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 ...

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 ...

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 ...

Algorithms are everywhere. The recipe for the frangipane cake is an algorithm. If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven. Now, wh ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

To study the resilience of distributed learning, the “Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes ...

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 ...

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 ...

The Dolev-Reischuk bound says that any deterministic Byzantine consensus protocol has (at least) quadratic communication complexity in the worst case. While it has been shown that the bound is tight in synchronous environments, it is still unknown whether ...

Classic synchronous consensus algorithms are leaderless in that processes exchange proposals, choose the maximum value and decide when they see the same choice across a couple of rounds. Indulgent consensus algorithms are typically leader-based. Although t ...

Distributed systems designers typically strive to improve performance and preserve availability despite failures or attacks; but, when strong consistency is also needed, they encounter fundamental limitations. The bottleneck is in replica coordination, whi ...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

Online services have become ubiquitous in technological society, the global demand for which has driven enterprises to construct gigantic datacenters that run their software. Such facilities have also recently become a substrate for third-party organizatio ...

In this paper, we consider the problem of cross-chain payment whereby customers of different escrows-implemented by a bank or a blockchain smart contract-successfully transfer digital assets without trusting each other. Prior to this work, cross-chain paym ...

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distr ...

The landscape of computing is changing, thanks to the advent of modern networking equipment that allows machines to exchange information in as little as one microsecond. Such advancement has enabled microsecond-scale distributed computing, where entire dis ...

The high computational costs of deep convolutional neural networks hinder their deployment in real-world applications, including pulmonary nodule detection from CT scans where large 3D image sizes amplify the issue. This paper presents a novel 3D method to ...

Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...

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 ...

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 ...

Genuinely Distributed Byzantine Machine Learning

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Genuinely distributed Byzantine machine learning

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

Byzantine Machine Learning: A Primer

Practical Byzantine-resilient Stochastic Gradient Descent

Scalable and Privacy-Preserving Federated Principal Component Analysis

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Byzantine Consensus is Θ(n^2): The Dolev-Reischuk Bound is Tight even in Partial Synchrony!

Leaderless consensus

Building Strongly-Consistent Systems Resilient to Failures, Partitions, and Slowdowns

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Hardware and Software Support for RPC-Centric Server Architecture

Cross-chain payment protocols with success guarantees

Toward Mobile Distributed Ledgers

Reliable Microsecond-Scale Distributed Computing

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

System Support for Robust Distributed Learning

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

Democratizing Machine Learning