Nastaran Abadi Khooshemehr;Mohammad Ali Maddah-Ali
{"title":"Vers: Coded Computing System With Distributed Encoding","authors":"Nastaran Abadi Khooshemehr;Mohammad Ali Maddah-Ali","doi":"10.1109/TIT.2025.3591523","DOIUrl":null,"url":null,"abstract":"Coded computing has proved to be useful in distributed computing, and has addressed challenges such as straggler workers. We have observed that almost all coded computing systems studied so far consider a setup of one leader and some workers. However, recently emerging technologies such as blockchain, internet of things, and federated learning introduce new requirements for coded computing systems. In these systems, data is generated (and probably stored) in a distributed manner, so central encoding/decoding by a leader is not feasible and scalable. This paper presents a multi-leader distributed coded computing system that consists of <inline-formula> <tex-math>$k\\in \\mathbb {N}$ </tex-math></inline-formula> data owners and <inline-formula> <tex-math>$N\\in \\mathbb {N}$ </tex-math></inline-formula> workers, where data owners employ workers to do some computations on their data, as specified by a target function <italic>f</i> of degree <inline-formula> <tex-math>$d\\in \\mathbb {N}$ </tex-math></inline-formula>. As there is no central encoder, workers perform encoding themselves, prior to computation phase. The challenge in this system is the presence of adversarial data owners that do not know the data of honest data owners but cause discrepancies by sending different versions of data to different workers, which is detrimental to local encodings in workers. There are at most <inline-formula> <tex-math>$\\beta \\in \\mathbb {N}$ </tex-math></inline-formula> adversarial data owners, and each distributes at most <inline-formula> <tex-math>$v\\in \\mathbb {N}$ </tex-math></inline-formula> different versions of data. Since the adversaries and their possibly colluded behavior are not known to workers and honest data owners, workers compute tags of their received data, in addition to their main computational task, and send them to data owners in order to help them in decoding. We introduce a tag function that allows data owners to partition workers into sets that previously had received the same data from all data owners. Then, we characterize the fundamental limit of this multi-leader distributed coded computing system, denoted by <inline-formula> <tex-math>$t^{*}$ </tex-math></inline-formula>, which is the minimum number of workers whose work can be used to correctly calculate the desired function of data of honest data owners. We show that <inline-formula> <tex-math>$t^{*}=v^{\\beta }d(K-1)+1$ </tex-math></inline-formula>, and present converse and achievable proofs.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 10","pages":"7609-7625"},"PeriodicalIF":2.9000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Information Theory","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11088248/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Coded computing has proved to be useful in distributed computing, and has addressed challenges such as straggler workers. We have observed that almost all coded computing systems studied so far consider a setup of one leader and some workers. However, recently emerging technologies such as blockchain, internet of things, and federated learning introduce new requirements for coded computing systems. In these systems, data is generated (and probably stored) in a distributed manner, so central encoding/decoding by a leader is not feasible and scalable. This paper presents a multi-leader distributed coded computing system that consists of $k\in \mathbb {N}$ data owners and $N\in \mathbb {N}$ workers, where data owners employ workers to do some computations on their data, as specified by a target function f of degree $d\in \mathbb {N}$ . As there is no central encoder, workers perform encoding themselves, prior to computation phase. The challenge in this system is the presence of adversarial data owners that do not know the data of honest data owners but cause discrepancies by sending different versions of data to different workers, which is detrimental to local encodings in workers. There are at most $\beta \in \mathbb {N}$ adversarial data owners, and each distributes at most $v\in \mathbb {N}$ different versions of data. Since the adversaries and their possibly colluded behavior are not known to workers and honest data owners, workers compute tags of their received data, in addition to their main computational task, and send them to data owners in order to help them in decoding. We introduce a tag function that allows data owners to partition workers into sets that previously had received the same data from all data owners. Then, we characterize the fundamental limit of this multi-leader distributed coded computing system, denoted by $t^{*}$ , which is the minimum number of workers whose work can be used to correctly calculate the desired function of data of honest data owners. We show that $t^{*}=v^{\beta }d(K-1)+1$ , and present converse and achievable proofs.
期刊介绍:
The IEEE Transactions on Information Theory is a journal that publishes theoretical and experimental papers concerned with the transmission, processing, and utilization of information. The boundaries of acceptable subject matter are intentionally not sharply delimited. Rather, it is hoped that as the focus of research activity changes, a flexible policy will permit this Transactions to follow suit. Current appropriate topics are best reflected by recent Tables of Contents; they are summarized in the titles of editorial areas that appear on the inside front cover.