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Asynchronous Byzantine Machine Learning (the case of SGD)

Related publications (32)

Planetary-Scale Byzantine Fault Tolerance

The scale and pervasiveness of the Internet make it a pillar of planetary communication, industry and economy, as well as a fundamental medium for public discourse and democratic engagement. In stark contrast with the Internet's decentralized infrastructur ...

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Cross-chain payment protocols with success guarantees

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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 ...

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Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

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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 ...

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Leaderless consensus

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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 ...

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Blockchain systems need to solve consensus despite the presence of rational users and failures. The notion of (k, t)-robustness is key to derive impossibility results with k rational players and t faulty players. However, these t faulty players are always ...

IEEE2021

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Classical synchronous consensus algorithms are leaderless: processes exchange their proposals, retain the maximum value and decide when they see the same choice across a couple of rounds. Indulgent consensus algorithms are more robust in that they only req ...

IEEE COMPUTER SOC2021

Leaderless Consensus

Rachid Guerraoui, Vincent Gramoli, Mihail Igor Zablotchi, Karolos Antoniadis, Antoine Philippe Matthieu Desjardins

Abstract—Classical synchronous consensus algorithms are leaderless: processes exchange their proposals, pick the max and decide when they see the same choice across a couple of rounds. Indulgent consensus algorithms are more robust in that they only req ...

2021

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 ...

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The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...

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