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Communication-Efficient Distributed Dual Coordinate Ascent

arXiv 1409.1458 ↗First posted 4 Sep 2014cs.LG math.OC stat.MLcited by 30

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Abstract (arXiv)

Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning. In this paper, we propose a communication-efficient framework, CoCoA, that uses local computation in a primal-dual setting to dramatically reduce the amount of necessary communication. We provide a strong convergence rate analysis for this class of algorithms, as well as experiments on real-world distributed datasets with implementations in Spark. In our experiments, we find that as compared to state-of-the-art mini-batch versions of SGD and SDCA algorithms, CoCoA converges to the same .001-accurate solution quality on average 25x as quickly.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2014.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 10; 2024: 5; 2025: 2; 2026: 1.

30 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2021An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systemscited by 32
  3. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  4. 2022Can 5th Generation Local Training Methods Support Client Sampling? Yes!cited by 22
  5. 2022Efficient Convex Optimization Requires Superlinear Memorycited by 14
  6. 2024FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneitycited by 13
  7. 2023Federated Empirical Risk Minimization via Second-Order Methodcited by 9
  8. 2023Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithmscited by 7
  9. 2022Personalized Federated Learning with Communication Compressioncited by 6
  10. 2021Linear Regression with Distributed Learning: A Generalization Error Perspectivecited by 4
  11. 2021Local and Global Uniform Convexity Conditionscited by 4
  12. 2022A Primal-Dual Algorithm for Hybrid Federated Learningcited by 4
18 more
  1. 2023Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviationcited by 4
  2. 2023RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learningcited by 4
  3. 2023FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learningcited by 3
  4. 2024Scalable Dual Coordinate Descent for Kernel Methodscited by 3
  5. 2021Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applicationscited by 2
  6. 2022FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differencescited by 2
  7. 2024Asynchronous Multi-Server Federated Learning for Geo-Distributed Clientscited by 2
  8. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  9. 2022Continual Learning with Distributed Optimization: Does CoCoA Forget?cited by 1
  10. 2024Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classificationcited by 1
  11. 2023On the Optimal Batch Size for Byzantine-Robust Distributed Learningcited by 0
  12. 2023Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Networkcited by 0
  13. 2023Personalized Federated Learning via ADMM with Moreau Envelopecited by 0
  14. 2023Distributed Continual Learning with CoCoA in High-dimensional Linear Regressioncited by 0
  15. 2024FedScalar: Federated Learning with Scalar Communication for Bandwidth-Constrained Networkscited by 0
  16. 2025On the Relationship Between CoCoA and ADMM for Distributed Empirical Risk Minimizationcited by 0
  17. 2025L-XAIDS: A LIME-based eXplainable AI framework for Intrusion Detection Systemscited by 0
  18. 2026Sheaf-Based Federated Representation Learningcited by 0