mlsanityData to 7 Oct 2026

A dual approach for federated learning

arXiv 2201.11183 ↗First posted 26 Jan 2022cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coordinate descent method developed by Necora et al.[Journal of Optimization Theory and Applications, 2017]. Additionally, we enhance the FedDCD method with inexact gradient oracles and Nesterov's acceleration. We demonstrate theoretically that our proposed approach achieves better convergence rates than the state-of-the-art primal federated optimization algorithms under certain situations. Numerical experiments on real-world datasets support our analysis.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 64 references that are in the map. First posted on arXiv 26 Jan 2022.

Builds on

24 of 64 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  7. 2014Convex Optimization: Algorithms and Complexitycited by 569
  8. 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 401
  9. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  10. 2021A Field Guide to Federated Optimizationcited by 312
  11. 2014EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimizationcited by 160
  12. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 152
12 more
  1. 2020Minibatch vs Local SGD for Heterogeneous Distributed Learningcited by 147
  2. 2017Optimal algorithms for smooth and strongly convex distributed optimization in networkscited by 81
  3. 2020Federated Composite Optimizationcited by 49
  4. 2018Optimal Algorithms for Non-Smooth Distributed Optimization in Networkscited by 41
  5. 2015Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selectioncited by 39
  6. 2013An Asynchronous Parallel Stochastic Coordinate Descent Algorithmcited by 26
  7. 2015Even Faster Accelerated Coordinate Descent Using Non-Uniform Samplingcited by 23
  8. 2014Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Propertiescited by 21
  9. 2013Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systemscited by 14
  10. 2013Inexact Coordinate Descent: Complexity and Preconditioningcited by 8
  11. 2020Decentralized Learning with Lazy and Approximate Dual Gradientscited by 6
  12. 2018Accelerating Greedy Coordinate Descent Methodscited by 4

Built on by

Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022Federated Learning via Inexact ADMMcited by 44
  2. 2022A Primal-Dual Algorithm for Hybrid Federated Learningcited by 4