# A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL

arXiv: https://arxiv.org/abs/2401.00809 · HTML: https://mlsanity.com/paper/2401.00809/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Peer-to-peer Federated Learning on Graphs](https://mlsanity.com/paper/1901.11173/) (2019)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019)
- [Deep Federated Learning for Autonomous Driving](https://mlsanity.com/paper/2110.05754/) (2021)
- [Decentralized Federated Learning via Mutual Knowledge Transfer](https://mlsanity.com/paper/2012.13063/) (2020)
- [Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data](https://mlsanity.com/paper/1912.12204/) (2019)
- [Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles](https://mlsanity.com/paper/2103.03786/) (2021)
- [Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems](https://mlsanity.com/paper/2010.08595/) (2020)

## Built on by (most cited first, 1 of 1)

- [Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources](https://mlsanity.com/paper/2411.16737/) (2024)

## Abstract (arXiv)

Federated Learning (FL) is a machine-learning approach enabling collaborative model training across multiple decentralized edge devices that hold local data samples, all without exchanging these samples. This collaborative process occurs under the supervision of a central server orchestrating the training or via a peer-to-peer network. The significance of FL is particularly pronounced in industries such as healthcare and finance, where data privacy holds paramount importance. However, training a model under the Federated learning setting brings forth several challenges, with one of the most prominent being the heterogeneity of data distribution among the edge devices. The data is typically non-independently and non-identically distributed (non-IID), thereby presenting challenges to model convergence. This report delves into the issues arising from non-IID and heterogeneous data and explores current algorithms designed to address these challenges.