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Speeding Up Distributed Machine Learning Using Codes
Open accessAbstract
Codes are widely used in many engineering applications to offerrobustnessagainstnoise. In large-scale systems, there are several types of noise that can affect the performance of distributed machine learning algorithms—straggler nodes, system failures, or communication bottlenecks—but there has been little interaction cutting across codes, machine learning, and distributed systems. In this paper, we provide theoretical insights on howcodedsolutions can achieve significant gains compared with uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms:matrix multiplicationanddata shuffling. For matrix multiplication, we use codes to alleviate the effect of stragglers and show that if the number of homogeneous workers is$n$, and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of$\log n$. For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction$\alpha $of the data matrix can be cached at each worker, and$n$is the number of workers,coded shufflingreduces the communication cost by a factor of$\left({\alpha + \frac {1}{n}}\right)\gamma (n)$compared with uncoded shuffling, where$\gamma (n)$is the ratio of the cost of unicasting$n$messages to$n$users to multicasting a common message (of the same size) to$n$users. For instance,$\gamma (n) \simeq n$if multicasting a message to$n$users is as cheap as unicasting a message to one user. We also provide experimental results, corroborating our theoretical gains of the coded algorithms.
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“Data poisoning attacks may occur in two ways such as clean-label attack (adversary can poison correct class of data sample) and dirty-label attack (adversary try to misclassify the target label of training dataset) [147], [148].”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [148]
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,917
Smith, D. K., & Bertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 12,385
(2010) · Cited by 4,411
Flagged first, then by citations
Research works printed but not linked: 3
- 10.1109/ISIT.2016.7541478 · DOI
- 2015, StarCluster
- 2015, Open MPI Open Source High Performance Computing
Cited by
Sorted by citations
Items 701–800 of 827
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Behrouzi-Far, A., & Soljanin, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.02415 · Cited by 1
Index Codes with Minimum Locality for \nThree Receiver Unicast Problems
Joy, S. K., & Natarajan, L. P. (2019) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · Cited by 1
Fountain Codes for Private Distributed Matrix-Matrix Multiplication
Bitar, R. (n.d.) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 1
Coded Privacy-Preserving Computation at Edge Networks.Preprint
Vedadi, E., Keshtkarjahromi, Y., & Seferoğlu, H. (2021) · arXiv (Cornell University) · Cited by 1
Wang, J., & Hu, G. (2021) · arXiv (Cornell University) · Cited by 1
Cheng, Z., Tang, L., Huang, Q., & Lee, P. P. C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4192493 · Cited by 1
Dynamic games for resource allocation in metaverse services and architectures
Han, Y. (2023) · doi:10.32657/10356/164497 · Cited by 1
On Locally Decodable Index CodesPreprint
Natarajan, L. P., Krishnan, P., & Lalitha, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.03895
Conceptualization of an Autonomic Machine Learning Platform for Non-Expert Developers
Lee, K. M., Yoo, J., & Hong, J. (2017) · WSEAS Transactions on Computers archive
Self-citationShi, J., Ling, C., Simeone, O., & Kliewer, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11698
A Fundamental Storage-Communication Tradeoff for Distributed Computing with Straggling NodesPreprint
Yan, Q., Wigger, M., Yang, S., & Tang, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07793
A Binary Randomized Coding Scheme for Pliable Index Coding with Multiple Requests
Song, L. (2018) · International Symposium on Turbo Codes and Iterative Information Processing · doi:10.1109/istc.2018.8625330
Robust Molecular Dynamics Simulations Using Coded FFT Algorithm
Wong, Y., Zhang, Y., Jeong, H., & Grover, P. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682276
Subspace-Aware Index CodesPreprint
Kailkhura, B., Theagarajan, L. N., & Varshney, P. K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.03589
The Capacity of Linear Computation Broadcast
Sun, H., & Jafar, S. A. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761676
Distributed Black-Box optimization via Error Correcting Codes
Bartan, B., & Pilancı, M. (2019) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2019.8919691
Wireless coded caching and computing
Amiri, M. M. (2019) · doi:10.25560/74101
Huang, W., Huang, J., Xia, J., Gao, Y., He, P., & He, L. (2019) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt47585.2019.8962442
Distributed caching and learning over wireless channels
Tegin, B. (2020) · Bilkent University Institutional Repository (Bilkent University)
Two Private Secure Distributed Coded Computation Schemes Using Extension Fields
Mahesh, A. A., Malladi, T. S., & Rajan, B. S. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148690
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Coding based Distributed Data Shuffling for Low Communication Cost in Data Center Networks
Liang, J., Yang, L., Wang, Z., Liu, X., & Wu, W. (2020) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn50589.2020.00119
Exploitation of Stragglers in Hierarchical Coded Matrix Multiplication
Kianidehkordi, S. (2020) · TSpace (University of Toronto)
$ε$-Approximate Coded Matrix Multiplication is Nearly Twice as Efficient as Exact MultiplicationPreprint
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Subramaniam, A. M. (2020)
PCRec: A Private Coding Computation Scheme Based on Edge Computing for Recommendation System
Zhai, M., Gu, F., Wang, J., Li, L., Lu, K., & Wang, J. (2020) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00049
Multi-layer Distributed Coding Solutions for Large-scale Distributed Computing
Yazdanialahabadi, A. (2020) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-ad8t-en72
Improved Private and Secure Distributed (Batch) Matrix MultiplicationPreprint
Li, J., & Hollanti, C. (2021) · arXiv (Cornell University)
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Hussain, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.10642
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Naderzadeh, Y., & Kliewer, J. (2021) · International Symposium on Turbo Codes and Iterative Information Processing · doi:10.1109/istc49272.2021.9594134
Lu, Z., Wang, J., Zhou, J., Wang, J., & Lu, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95384-3_33
The Roaming Edge and its Applications
Banerjee, S., Arpaci-Dusseau, R. H., Dai, S., Fawaz, K., Gupta, M., Lee, K., & Venkataraman, S. (2022) · GetMobile Mobile Computing and Communications · doi:10.1145/3529706.3529708
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(2021) · Issues in Information Systems · doi:10.48009/2_iis_2021_252-263
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Chang, J., Wang, J., Gu, F., Lu, K., Li, L., & Wang, J. (2021) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom53373.2021.00060
Coded Distributed Computing Schemes via Grouping Method
Jiang, J., Zhou, L., & Qu, L. (2022) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3532213.3532228
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Li, T., Seferoğlu, H., & Koyuncu, E. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001480
Zou, X., Wang, J., Liu, C., Li, L., Gu, F., & Li, G. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-24383-7_12
Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo
Chittoor, H. H. S., & Simeone, O. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001070
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Ji, Z., Chen, L., Chen, X., & Wei, G. (2022) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp55476.2022.10039243
Robust incentive mechanisms for efficient and secure coded edge intelligence
Ng, J. S. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/164596
A Grouping Design for Cascaded Coded Distributed Computing
Dong, H., & Jiang, J. (2022) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc56324.2022.10065803
BYEE: Batch Fully Private Scheme for Coded Distributed Matrix Multiplication
Li, G., Wang, J., Zou, X., Li, L., & Gu, F. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00112
Li, L., & Wang, F. (2023) · SpringerBriefs in service science · doi:10.1007/978-3-031-25333-1_6
Coded computing for distributed graph-based semi-supervised learning
Tan, S., Chen, L., & Wang, W. (2023) · JUSTC · doi:10.52396/justc-2022-0133
Folded Polynomial Codes for Coded Distributed AA⊤-Type Matrix Multiplication
Xu, J., Zhang, Y., & Wang, L. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3286420
Efficient Construction of Encoding Polynomials in a Distributed Coded Computing Scheme
HIBINO, D., & SHIBUYA, T. (2023) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2023tap0011
Stragglers in Distributed Matrix Multiplication
Nissim, R., & Schwartz, O. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43943-8_4
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Wang, S., Yan, Q., Zhang, J., Wang, J., & Song, L. (2023) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp58612.2023.10404479
Kamalesh, K., & Natesan, D. G. (2024) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.5.0324.0786
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Qin, K., Gu, S., Zhang, Z., Zhang, Q., & Xiang, W. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10571074
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Metadata
- Authors
- Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., & Ramchandran, K.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- University of California, Berkeley (US)
- University of California, Santa Barbara (US)
- University of Wisconsin–Madison (US)
- Venue
- IEEE Transactions on Information Theory
- Published
- 2018-03
- Volume
- 64
- Issue
- 3
- Pages
- 1514-1529
- ISSN
- 0018-9448 (ISSN-L)
- 1557-9654
- OpenAlex
- W2268702383
- Cited by
- 829
- References
- 122
- Citations by year
- 2026: 24 · 2025: 56 · 2024: 79 · 2023: 101 · 2022: 111 · 2021: 148 · 2020: 130 · 2019: 105 · 2018: 48
- FWCI
- 54.46
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Information Theory (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Institute for Information & communications Technology Promotion(IITP) grant
- Korea government(MSIT) (2017-0-00694)
- Brain Korea 21 Plus Project, and NSF CIF grant (1703678)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Data Storage Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- distributed learning, coded computing, straggler nodes, distributed matrix multiplication, data shuffling, communication bottleneck, multicast, caching
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