# A dual approach for federated learning

arXiv: https://arxiv.org/abs/2201.11183 · HTML: https://mlsanity.com/paper/2201.11183/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives](https://mlsanity.com/paper/1407.0202/) (2014)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization](https://mlsanity.com/paper/1404.6264/) (2014)
- [Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning](https://mlsanity.com/paper/2008.03606/) (2020)
- [Minibatch vs Local SGD for Heterogeneous Distributed Learning](https://mlsanity.com/paper/2006.04735/) (2020)
- [Optimal algorithms for smooth and strongly convex distributed optimization in networks](https://mlsanity.com/paper/1702.08704/) (2017)
- [Federated Composite Optimization](https://mlsanity.com/paper/2011.08474/) (2020)
- [Optimal Algorithms for Non-Smooth Distributed Optimization in Networks](https://mlsanity.com/paper/1806.00291/) (2018)
- [Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection](https://mlsanity.com/paper/1506.00552/) (2015)
- [An Asynchronous Parallel Stochastic Coordinate Descent Algorithm](https://mlsanity.com/paper/1311.1873/) (2013)
- [Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling](https://mlsanity.com/paper/1512.09103/) (2015)
- [Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties](https://mlsanity.com/paper/1403.3862/) (2014)
- [Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems](https://mlsanity.com/paper/1305.1922/) (2013)
- [Inexact Coordinate Descent: Complexity and Preconditioning](https://mlsanity.com/paper/1304.5530/) (2013)
- [Decentralized Learning with Lazy and Approximate Dual Gradients](https://mlsanity.com/paper/2008.01748/) (2020)
- [Accelerating Greedy Coordinate Descent Methods](https://mlsanity.com/paper/1806.02476/) (2018)

## Built on by (most cited first, 3 of 3)

- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [A Primal-Dual Algorithm for Hybrid Federated Learning](https://mlsanity.com/paper/2210.08106/) (2022)
- [From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning](https://mlsanity.com/paper/2609.02984/) (2026)

## 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.