Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing

Jahani-Nezhad, Tayyebeh; Maddah-Ali, Mohammad Ali

doi:10.1109/tpami.2022.3151434

Cited by 12 publications

(12 citation statements)

References 31 publications

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“…For example, in coded computing algorithms, we can divide a task into several parts and outsource it to several servers. If some of these servers are stragglers or malignant, the computation's final answer can still be recovered [107].…”

Section: B Edge Computing Challengesmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

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In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.

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Section: B Edge Computing Challengesmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Inspired by Barycentric polynomial interpolation [27], [44], the input data {D i,j } Ki−1 j=0 for set i is linearly encoded through function g i (x) as:…”

Section: ) Encodingmentioning

confidence: 99%

“…3) Decoding: For set i, the master decodes using function r i (x), which is constructed by interpolation [27], [44] as:…”

Section: ) Encodingmentioning

confidence: 99%

“…However, LCC still suffers from several shortcomings [27]. First, the recovery threshold is proportional to the degree of polynomial functions, which can be prohibitively large for complex tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Second, Lagrange polynomial interpolation can be ill-conditioned, making it challenging to ensure the numerical stability. In [27], Berrut's Approximated Coded Computing (BACC) is proposed to address these shortcomings and further expand the scope of computation tasks to arbitrary functions. However, BACC only yields approximated computing results and does not guarantee data privacy preservation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Capacity of Privacy-Preserving and Straggler-Robust Distributed Coded Computing

Zeng

Zhou

2021

2021 IEEE/CIC International Conference on Communications in China (ICCC)

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Distributed computing is known as an emerging and efficient technique to support various intelligent services, such as large-scale machine learning. However, privacy leakage and random delays from straggling servers pose significant challenges. To address these issues, coded computing, a promising solution that combines coding theory with distributed computing, recovers computation tasks with results from a subset of workers. In this paper, we propose the adaptive privacypreserving coded computing (APCC) strategy, which can adaptively provide accurate or approximated results according to the form of computation functions, so as to suit diverse types of computation tasks. We prove that APCC achieves complete data privacy preservation and demonstrate its optimality in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding. To further alleviate the straggling effect and reduce delay, we integrate hierarchical task partitioning and task cancellation into the coding design of APCC. The corresponding partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. Simulation results show that APCC can reduce task completion delay by at least 42.9% compared to other state-of-the-art benchmarks.

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Secure and Private Approximated Coded Distributed Computing Using Elliptic Curve Cryptography

Qiu,

Zhu

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing

Cited by 12 publications

References 31 publications

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

On the Capacity of Privacy-Preserving and Straggler-Robust Distributed Coded Computing

Secure and Private Approximated Coded Distributed Computing Using Elliptic Curve Cryptography

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