# FedCD: Improving Performance in non-IID Federated Learning

arXiv: https://arxiv.org/abs/2006.09637 · HTML: https://mlsanity.com/paper/2006.09637/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [Federated Learning with GAN-based Data Synthesis for Non-IID Clients](https://mlsanity.com/paper/2206.05507/) (2022)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Feature Matching Data Synthesis for Non-IID Federated Learning](https://mlsanity.com/paper/2308.04761/) (2023)
- [Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis](https://mlsanity.com/paper/2110.07567/) (2021)
- [Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning](https://mlsanity.com/paper/2107.07233/) (2021)
- [TinyFedTL: Federated Transfer Learning on Tiny Devices](https://mlsanity.com/paper/2110.01107/) (2021)
- [Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection](https://mlsanity.com/paper/2310.17097/) (2023)
- [FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer](https://mlsanity.com/paper/2305.01154/) (2023)
- [Personalized federated learning based on feature fusion](https://mlsanity.com/paper/2406.16583/) (2024)
- [Data privacy protection in microscopic image analysis for material data mining](https://mlsanity.com/paper/2111.07892/) (2021)
- [Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data](https://mlsanity.com/paper/2207.07493/) (2022)
- [CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification](https://mlsanity.com/paper/2510.17584/) (2025)
- [Benchmark Evaluation of Federated Learning on Multi-organ Images](https://mlsanity.com/paper/2607.08219/) (2026)

## Abstract (arXiv)

Federated learning has been widely applied to enable decentralized devices, which each have their own local data, to learn a shared model. However, learning from real-world data can be challenging, as it is rarely identically and independently distributed (IID) across edge devices (a key assumption for current high-performing and low-bandwidth algorithms). We present a novel approach, FedCD, which clones and deletes models to dynamically group devices with similar data. Experiments on the CIFAR-10 dataset show that FedCD achieves higher accuracy and faster convergence compared to a FedAvg baseline on non-IID data while incurring minimal computation, communication, and storage overheads.