The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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 301–400 of 827
Numerically stable coded matrix computations via circulant and rotation matrix embeddings
Ramamoorthy, A., & Tang, L. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517750 · Cited by 12
Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees
Xhemrishi, M., Bitar, R., & Wachter-Zeh, A. (2022) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit50566.2022.9834805 · Cited by 12
Federated Optimization of Smooth Loss Functions
Jadbabaie, A., Makur, A., & Devavrat Shah, B. (2023) · IEEE Transactions on Information Theory · doi:10.1109/tit.2023.3317168 · Cited by 12
Multi-Message Gradient Coding for Utilizing Non-Persistent Stragglers
Tauz, L., & Dolecek, L. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048902 · Cited by 12
Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning
Kadhe, S., Koyluoglu, O. O., & Ramchandran, K. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174120 · Cited by 12
Self-citationCache-Aided Matrix Multiplication Retrieval
Wan, K., Sun, H., Ji, M., Tuninetti, D., & Caire, G. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3157835 · Cited by 12
Coded Computing via Binary Linear Codes: Designs and Performance Limits
Soleymani, M., Jamali, M. V., & Mahdavifar, H. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3103772 · Cited by 12
A Systematic Approach Towards Efficient Private Matrix Multiplication
Zhu, J., & Li, S. (2022) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2022.3181144 · Cited by 13
Analog Secret Sharing With Applications to Private Distributed Learning
Soleymani, M., Mahdavifar, H., & Avestimehr, A. S. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3173417 · Cited by 12
Secure Coded Cooperative Computation at the Heterogeneous Edge against Byzantine Attacks
Keshtkarjahromi, Y., Bitar, R., Dasari, V. R., Rouayheb, S. E., & Seferoğlu, H. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013340 · Cited by 12
Multi-Access Distributed Computing
Brunero, F., & Elia, P. (2024) · IEEE Transactions on Information Theory · doi:10.1109/tit.2024.3373128 · Cited by 11
Distributed Learning Based on 1-Bit Gradient Coding in the Presence of Stragglers
Li, C., & Skoglund, M. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3377715 · Cited by 12
Coded Distributed Computing for Vehicular Edge Computing With Dual-Function Radar Communication
Nguyễn, T. H., Thi, H. L. N., Hoang, H. L., Tan, J., Luong, N. C., Xiao, S., Niyato, D. T., & Kim, D. I. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3409554 · Cited by 11
Coded Distributed Computing over Packet Erasure Channels
Han, D.-J., Sohn, J.-Y., & Moon, J. J. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849747 · Cited by 11
Compressed Coded Distributed Computing
Elkordy, A. R., Li, S., Maddah-Ali, M. A., & Avestimehr, A. S. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3054906 · Cited by 11
CCOS: A Coded Computation Offloading Strategy for Satellite-Terrestrial Integrated Networks
Pang, B., Gu, S., Zhang, Q., Zhang, N., & Xiang, W. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498862 · Cited by 11
Low-Latency Distributed Inference at the Network Edge Using Rateless Codes (Invited Paper)
Frigård, A., Kumar, S., Rosnes, E., & Amat, A. G. I. (2021) · International Symposium on Wireless Communication Systems · doi:10.1109/iswcs49558.2021.9562193 · Cited by 11
On the Fundamental Limits of Coded Data Shuffling
Elmahdy, A., & Mohajer, S. (2018) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2018.8437672 · Cited by 11
PRAC: private and rateless adaptive coded computation at the edge
Bitar, R., Xing, Y., Keshtkarjahromi, Y., Dasari, V. R., Rouayheb, S. E., & Seferoğlu, H. (2019) · Disruptive Technologies in Information Sciences · doi:10.1117/12.2519768 · Cited by 11
Irregular Product Coded Computation for High-Dimensional Matrix Multiplication
Park, H., & Moon, J. J. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849236 · Cited by 11
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
Fan, X., Soto, P., Zhong, X., Xi, D., Wang, Y., & Li, J. (2020) · International Workshop on Quality of Service · doi:10.1109/iwqos49365.2020.9213028 · Cited by 11
Distributed SignSGD With Improved Accuracy and Network-Fault Tolerance
Phong, L. T., & Phuong, T. T. (2020) · IEEE Access · doi:10.1109/access.2020.3032637 · Cited by 11
Optimal Incentive and Load Design for Distributed Coded Machine Learning
Ding, N., Fang, Z., Duan, L., & Huang, J. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3078494 · Cited by 11
Wang, J., & Hu, G. (2022) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.6048 · Cited by 11
A Droplet Approach Based on Raptor Codes for Distributed Computing With Straggling Servers
Severinson, A., Amat, A. G. I., Rosnes, E., Lázaro, F., & Liva, G. (2018) · International Symposium on Turbo Codes and Iterative Information Processing · doi:10.1109/istc.2018.8625292 · Cited by 11
Dynamic backup workers for parallel machine learning
Xu, C., Neglia, G., & Sebastianelli, N. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107846 · Cited by 11
Parity Models: A General Framework for Coding-Based Resilience in ML InferencePreprint
Kosaian, J., Rashmi, K. V., & Venkataraman, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00863 · Cited by 11
Heterogeneity Shifts the Storage-Computation Tradeoff in Secure Multi-Cloud Systems
Chen, J., Sung, C. W., & Chan, T. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3206868 · Cited by 10
Cheng, Y., Zhao, Y., Zhao, G., & Xu, H. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109777 · Cited by 10
Bivariate Polynomial Coding for Straggler Exploitation with Heterogeneous Workers
Hasırcıoglu, B., Gómez-Vilardebó, J., & Gündüz, D. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174542 · Cited by 10
Hierarchical Broadcast Coding: Expediting Distributed Learning at the Wireless Edge
Han, D.-J., Sohn, J.-Y., & Moon, J. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3040792 · Cited by 6
Communication-Efficient Coded Computing for Distributed Multi-Task Learning
Hu, H., Wu, Y., Shi, Y., Li, S., Jiang, C., & Zhang, W. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3275194 · Cited by 10
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Soto, P., Li, J., & Fan, X. (2019) · International Conference on Machine Learning · Cited by 10
Random Convolutional Coding for Robust and Straggler Resilient Distributed Matrix Computation.
Das, A. B., Ramamoorthy, A., & Vaswani, N. (2019) · Iowa State University Digital Repository (Iowa State University) · Cited by 10
Sahraei, S., Quinton, P., & Gastpar, M. (2019) · IEEE Transactions on Information Theory · doi:10.1109/tit.2019.2930692 · Cited by 10
Li, K., Tao, M., Zhang, J., & Simeone, O. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174273 · Cited by 10
Sequential addition of coded sub-tasks for straggler mitigation
Badita, A., Parag, P., & Aggarwal, V. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155358 · Cited by 10
A Distributed Low-Complexity Coding Solution for Large-Scale Distributed FFT
Yazdanialahabadi, A., & Ardakani, M. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3016648 · Cited by 10
Serverless Straggler Mitigation using Error-Correcting Codes
Gupta, V., Carrano, D., Yang, Y. L., Shankar, V., Courtade, T. A., & Ramchandran, K. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00019 · Cited by 10
Self-citationHierarchical Coded Elastic Computing
Kiani, S., Adikari, T. B., & Draper, S. C. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414863 · Cited by 10
Flexible Distributed Matrix Multiplication
Li, W., Chen, Z., Wang, Z., Jafar, S. A., & Jafarkhani, H. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3204488 · Cited by 10
Coded Sequential Matrix Multiplication for Straggler Mitigation
Krishnan, M. N., Hosseini, E., & Khisti, A. J. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3104970 · Cited by 10
ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems
Soleymani, M., Ali, R. E., Mahdavifar, H., & Avestimehr, A. S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20809 · Cited by 11
Combating Computational Heterogeneity in Large-Scale Distributed Computing via Work ExchangePreprint
Attia, M. A., & Tandon, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08452 · Cited by 10
Coded Computing for Federated Learning at the EdgePreprint
Prakash, S., Dhakal, S., Akdeniz, M. R., Avestimehr, A. S., & Himayat, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03273 · Cited by 10
On Allocation of Systematic Blocks in Coded Distributed Computing
Ardakani, M. H., Mehrabi, M., Ardakani, M., & Tellambura, C. (2022) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3140100 · Cited by 9
The Optimal Rate of MDS Variable Generation
Zhao, Y., & Sun, H. (2023) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit54713.2023.10206914 · Cited by 9
The Capacity of 3 User Linear Computation Broadcast
Yao, Y., & Jafar, S. A. (2024) · IEEE Transactions on Information Theory · doi:10.1109/tit.2024.3392685 · Cited by 9
Verifiable Coded Computation of Multiple Functions
Kim, W., Kruglik, S., & Kiah, H. M. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3450288 · Cited by 9
Coded Computation Over Heterogeneous Workers With Random Task Arrivals
Zhang, F., Sun, Y., & Zhou, S. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3067888 · Cited by 9
Fundamental Limits of Distributed Linearly Separable Computation under Cyclic Assignment
Huang, W., Wan, K., Sun, H., Ji, M., Qiu, R. C., & Caire, G. (2023) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit54713.2023.10206661 · Cited by 9
Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding
Zhang, T., Liu, K., Kosaian, J., Yang, J., & Vinayak, R. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611479.3611514 · Cited by 9
Gradient Delay Analysis in Asynchronous Distributed Optimization
Al-Lawati, H., & Draper, S. C. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054587 · Cited by 9
Coded Computing in Unknown Environment via Online Learning
Yang, C.-S., Pedarsani, R., & Avestimehr, A. S. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174281 · Cited by 8
Self-citationFully Private Coded Matrix Multiplication From Colluding Workers
Kim, M., Yang, H., & Lee, J. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.3037744 · Cited by 9
Taming Time-Varying Information Asymmetry in Fresh Status Acquisition
Wang, Z., Gao, L. X., & Huang, J. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488884 · Cited by 9
Stream Iterative Distributed Coded Computing for Learning Applications in Heterogeneous Systems
Esfahanizadeh, H., Cohen, A., & Médard, M. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796977 · Cited by 9
On Distributed Computing With Heterogeneous Communication Constraints
Shakya, N., Li, F., & Chen, J. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3181234 · Cited by 9
Analog Secure Distributed Matrix Multiplication over Complex Numbers
Makkonen, O., & Hollanti, C. (2022) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit50566.2022.9834873 · Cited by 9
Orthonormal Sketches for Secure Coded Regression
Charalambides, N., Mahdavifar, H., Pilancı, M., & Hero, A. O. (2022) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit50566.2022.9834356 · Cited by 9
Sparse Random Khatri-Rao Product Codes for Distributed Matrix Multiplication
Ji, R., Heidarzadeh, A., & Narayanan, K. R. (2022) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw54588.2022.9965842 · Cited by 9
Download and Access Trade-offs in Lagrange Coded Computing
Raviv, N., Yu, Q., Bruck, J., & Avestimehr, S. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849547 · Cited by 9
A Computation-Communication Tradeoff Study for Mobile Edge Computing Networks
Li, K., Tao, M., & Chen, Z. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849601 · Cited by 9
Rateless Codes for Distributed Computations with Sparse Compressed Matrices
Mallick, A., & Joshi, G. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849306 · Cited by 9
Optimizing the Transition Waste in Coded Elastic Computing
Dau, S. H., Gabrys, R., Huang, Y.-C., Feng, C., Luu, Q.-H., Alzahrani, E. J., & Tari, Z. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174521 · Cited by 9
Coded Secure Multi-Party Computation for Massive Matrices with Adversarial Nodes
Najarkolaei, S. R. H., Maddah-Ali, M. A., & Aref, M. R. (2020) · Iran Workshop on Communication and Information Theory (IWCIT) · doi:10.1109/iwcit50667.2020.9163533 · Cited by 9
GCSA Codes with Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication
Chen, Z., Jia, Z., Wang, Z., & Jafar, S. A. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174230 · Cited by 12
Heterogeneous Computation Assignments in Coded Elastic Computing
Woolsey, N., Chen, R., & Ji, M. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174275 · Cited by 9
On Gradient Coding with Partial Recovery
Sarmasarkar, S., Lalitha, V., & Karamchandani, N. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517849 · Cited by 9
Coding Method for Parallel Iterative Linear SolverPreprint
Yang, Y. L., Grover, P., & Kar, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00163 · Cited by 10
CodeNet: Training Large Scale Neural Networks in Presence of Soft-ErrorsPreprint
Dutta, S., Bai, Z., Low, T. M., & Grover, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.01042 · Cited by 13
Straggler-Aware Gradient Aggregation for Large-Scale Distributed Deep Learning System
Li, Y., Huang, J., Li, Z., Liu, J., Zhou, S., Zhang, T., Jiang, W., & Wang, J. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3441039 · Cited by 9
Tessellated Distributed Computing
Khalesi, A., & Elia, P. (2025) · IEEE Transactions on Information Theory · doi:10.1109/tit.2025.3556384 · Cited by 8
Distributed Encoding and Updating for SAZD Coded Distributed Training
Dai, M., Yuan, J., Huang, Q., Lin, X., & Wang, H. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3276888 · Cited by 8
DxPU: Large-scale Disaggregated GPU Pools in the Datacenter
He, B., Zheng, X., Chen, Y., Li, W., Zhou, Y., Long, X., Zhang, P., Lu, X., Jiang, L., Liu, Q., et al. (2023) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3617995 · Cited by 8
A High-Throughput and Secure Coded Blockchain for IoT
Taherpour, A., & Wang, X. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3532850 · Cited by 13
Coded Data Rebalancing: Fundamental Limits and Constructions
Krishnan, P., Lalitha, V., & Natarajan, L. P. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174482 · Cited by 8
Nguyen, Q. M., Jeong, H., & Grover, P. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9173985 · Cited by 8
Gradient Coding with Dynamic Clustering for Straggler Mitigation
Buyukates, B., Özfatura, E., Ulukuş, Ş., & Gündüz, D. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500346 · Cited by 8
The Discrepancy Attack on Polyshard-ed Blockchains
Khooshemehr, N. A., & Maddah-Ali, M. A. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517994 · Cited by 7
Stream Distributed Coded Computing
Cohen, A., Thiran, G., Esfahanizadeh, H., & Médard, M. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3102279 · Cited by 8
Adaptive and Heterogeneity-Aware Coded Cooperative Computation at the Edge
Keshtkarjahromi, Y., Xing, Y., & Seferoğlu, H. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3106250 · Cited by 8
Client Scheduling for Federated Learning over Wireless Networks: A Submodular Optimization Approach
Ye, L., & Gupta, V. (2021) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc45484.2021.9683685 · Cited by 8
Distributed Matrix Multiplication Based on Frame Quantization for Straggler Mitigation
Son, K., & Choi, W. (2022) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2022.3182221 · Cited by 8
Hong, S., Yang, H., & Lee, J. (2020) · IEEE Access · doi:10.1109/access.2020.3031590 · Cited by 8
Adaptive Coding for Matrix Multiplication at Edge Networks
Vedadi, E., & Seferoğlu, H. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517801 · Cited by 8
A locality-based approach for coded computationPreprint
Rudow, M., Rashmi, K. V., & Guruswami, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02440 · Cited by 8
Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devices
Tang, W., Li, J., Chen, L., & Chen, X. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3418901 · Cited by 7
HASFL: Heterogeneity-Aware Split Federated Learning Over Edge Computing Systems
Lin, Z., Chen, Z., Chen, X., Ni, W., & Gao, Y. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3673358 · Cited by 7
On Gradient Coding With Partial Recovery
Sarmasarkar, S., Lalitha, V., & Karamchandani, N. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3230779 · Cited by 7
Compression-Informed Coded Computing
Rudow, M., Charalambides, N., Hero, A. O., & Rashmi, K. V. (2023) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit54713.2023.10206890 · Cited by 7
Randomized Polar Codes for Anytime Distributed Machine Learning
Bartan, B., & Pilancı, M. (2023) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2023.3310931 · Cited by 7
DRL-Based Workload Allocation for Distributed Coded Machine Learning
Zhou, Y.-T., Ye, Q., & Huang, H. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278586 · Cited by 7
Frame Codes For Distributed Coded Computation
Yosibash, R., & Zamir, R. (2021) · International Symposium on Turbo Codes and Iterative Information Processing · doi:10.1109/istc49272.2021.9594259 · Cited by 7
Flexible Constructions for Distributed Matrix Multiplication
Li, W., Chen, Z., Wang, Z., Jafar, S. A., & Jafarkhani, H. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517963 · Cited by 7
Liu, N., Li, K., & Tao, M. (2021) · China Communications · doi:10.23919/jcc.2021.10.002 · Cited by 7
Cheng, S. (2022) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2022.101745 · Cited by 7
Location Privacy-Aware Coded Offloading for Distributed Edge Computing
He, Y., & He, X. (2022) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798165 · Cited by 7
Edge Computing-Aided Coded Vertical Federated Linear Regression
Dai, M., Zheng, Z., Hong, Z., Zhang, S., & Wang, H. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3174615 · Cited by 7
Coded Distributed Computing With Predictive Heterogeneous User Demands: A Learning Auction Approach
Zhu, K., Liang, J., Li, J., & Yi, C. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3180811 · Cited by 7
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
