# FedFMC: Sequential Efficient Federated Learning on Non-iid Data

arXiv: https://arxiv.org/abs/2006.10937 · HTML: https://mlsanity.com/paper/2006.10937/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare](https://mlsanity.com/paper/2206.08516/) (2022)
- [FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition](https://mlsanity.com/paper/2404.18848/) (2024)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Privacy and Trust Redefined in Federated Machine Learning](https://mlsanity.com/paper/2103.15753/) (2021)
- [Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients](https://mlsanity.com/paper/2201.10899/) (2022)
- [Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments](https://mlsanity.com/paper/2110.11006/) (2021)
- [FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data](https://mlsanity.com/paper/2205.09305/) (2022)
- [Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data](https://mlsanity.com/paper/2412.13757/) (2024)
- [Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum](https://mlsanity.com/paper/2311.18578/) (2023)
- [Data privacy protection in microscopic image analysis for material data mining](https://mlsanity.com/paper/2111.07892/) (2021)
- [Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets](https://mlsanity.com/paper/2204.11017/) (2022)
- [FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning](https://mlsanity.com/paper/2402.09095/) (2024)
- [MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning](https://mlsanity.com/paper/2404.13421/) (2024)
- [UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks](https://mlsanity.com/paper/2408.07075/) (2024)
- [Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention](https://mlsanity.com/paper/2605.11752/) (2026)

## Abstract (arXiv)

As a mechanism for devices to update a global model without sharing data, federated learning bridges the tension between the need for data and respect for privacy. However, classic FL methods like Federated Averaging struggle with non-iid data, a prevalent situation in the real world. Previous solutions are sub-optimal as they either employ a small shared global subset of data or greater number of models with increased communication costs. We propose FedFMC (Fork-Merge-Consolidate), a method that dynamically forks devices into updating different global models then merges and consolidates separate models into one. We first show the soundness of FedFMC on simple datasets, then run several experiments comparing against baseline approaches. These experiments show that FedFMC substantially improves upon earlier approaches to non-iid data in the federated learning context without using a globally shared subset of data nor increase communication costs.