# Coded Federated Learning

arXiv: https://arxiv.org/abs/2002.09574 · HTML: https://mlsanity.com/paper/2002.09574/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning](https://mlsanity.com/paper/2201.02932/) (2022)
- [FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning](https://mlsanity.com/paper/2107.04271/) (2021)
- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (2023)
- [CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework](https://mlsanity.com/paper/2104.08184/) (2021)
- [Stochastic Coded Federated Learning with Convergence and Privacy Guarantees](https://mlsanity.com/paper/2201.10092/) (2022)
- [Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design](https://mlsanity.com/paper/2211.04132/) (2022)
- [Coded-InvNet for Resilient Prediction Serving Systems](https://mlsanity.com/paper/2106.06445/) (2021)
- [Dynamic Network-Assisted D2D-Aided Coded Distributed Learning](https://mlsanity.com/paper/2111.14789/) (2021)
- [FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation at the Edge Nodes](https://mlsanity.com/paper/2111.01074/) (2021)
- [Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning](https://mlsanity.com/paper/2206.02450/) (2022)
- [Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks](https://mlsanity.com/paper/2303.10254/) (2023)
- [Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation](https://mlsanity.com/paper/2403.14905/) (2024)
- [Coded Matrix Computations for D2D-enabled Linearized Federated Learning](https://mlsanity.com/paper/2302.12305/) (2023)

## Abstract (arXiv)

Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data. The convergence performance of federated learning is severely impacted in heterogeneous computing platforms such as those at the wireless edge, where straggling computations and communication links can significantly limit timely model parameter updates. This paper develops a novel coded computing technique for federated learning to mitigate the impact of stragglers. In the proposed Coded Federated Learning (CFL) scheme, each client device privately generates parity training data and shares it with the central server only once at the start of the training phase. The central server can then preemptively perform redundant gradient computations on the composite parity data to compensate for the erased or delayed parameter updates. Our results show that CFL allows the global model to converge nearly four times faster when compared to an uncoded approach