mlsanityData to 7 Oct 2026

COLA: Decentralized Linear Learning

arXiv 1808.04883 ↗First posted 13 Aug 2018cs.DC cs.LG stat.MLcited by 30

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Abstract (arXiv)

Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device, on an arbitrary communication network, without a central coordinator. We propose COLA, a new decentralized training algorithm with strong theoretical guarantees and superior practical performance. Our framework overcomes many limitations of existing methods, and achieves communication efficiency, scalability, elasticity as well as resilience to changes in data and participating devices.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 7; 2023: 8; 2024: 3; 2025: 2; 2026: 2.

30 papers.

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  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2022An Improved Analysis of Gradient Tracking for Decentralized Machine Learningcited by 68
  3. 2021Accelerating Gossip SGD with Periodic Global Averagingcited by 25
  4. 2021Distributed Learning Systems with First-order Methodscited by 17
  5. 2021Towards Demystifying Serverless Machine Learning Trainingcited by 16
  6. 2024Byzantine-Robust Aggregation for Securing Decentralized Federated Learningcited by 7
  7. 2021Federated CycleGAN for Privacy-Preserving Image-to-Image Translationcited by 5
  8. 2022Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Settingcited by 4
  9. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  10. 2023Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionalitycited by 3
  11. 2023Train 'n Trade: Foundations of Parameter Marketscited by 3
  12. 2023A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learningcited by 2
18 more
  1. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  2. 2025Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resiliencecited by 2
  3. 2026Decentralized Proximal Stochastic Gradient Langevin Dynamicscited by 2
  4. 2021Decentralized Statistical Inference with Unrolled Graph Neural Networkscited by 1
  5. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  6. 2022Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descentcited by 1
  7. 2022Continual Learning with Distributed Optimization: Does CoCoA Forget?cited by 1
  8. 2023Harnessing the Power of Federated Learning in Federated Contextual Banditscited by 1
  9. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  10. 2021Generalized ADMM in Distributed Learning via Variational Inequalitycited by 0
  11. 2021Decentralized Feature-Distributed Optimization for Generalized Linear Modelscited by 0
  12. 2021Federated Learning with Superquantile Aggregation for Heterogeneous Datacited by 0
  13. 2022Online Decentralized Frank-Wolfe: From theoretical bound to applications in smart-buildingcited by 0
  14. 2023Secure Decentralized Learning with Blockchaincited by 0
  15. 2023Federated Learning for Sparse Principal Component Analysiscited by 0
  16. 2023Distributed Continual Learning with CoCoA in High-dimensional Linear Regressioncited by 0
  17. 2025On the Relationship Between CoCoA and ADMM for Distributed Empirical Risk Minimizationcited by 0
  18. 2026DeRelayL: Sustainable Decentralized Relay Learningcited by 0