# Prototype Helps Federated Learning: Towards Faster Convergence

arXiv: https://arxiv.org/abs/2303.12296 · HTML: https://mlsanity.com/paper/2303.12296/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2106.05001/) (2021)

## Built on by (most cited first, 7 of 7)

- [MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence](https://mlsanity.com/paper/2304.01950/) (2023)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality](https://mlsanity.com/paper/2401.07558/) (2024)
- [Boosting Federated Learning Convergence with Prototype Regularization](https://mlsanity.com/paper/2307.10575/) (2023)
- [Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence](https://mlsanity.com/paper/2501.15257/) (2025)
- [EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning](https://mlsanity.com/paper/2310.13367/) (2023)
- [Towards efficient compression and communication for prototype-based decentralized learning](https://mlsanity.com/paper/2411.09267/) (2024)

## Abstract (arXiv)

Federated learning (FL) is a distributed machine learning technique in which multiple clients cooperate to train a shared model without exchanging their raw data. However, heterogeneity of data distribution among clients usually leads to poor model inference. In this paper, a prototype-based federated learning framework is proposed, which can achieve better inference performance with only a few changes to the last global iteration of the typical federated learning process. In the last iteration, the server aggregates the prototypes transmitted from distributed clients and then sends them back to local clients for their respective model inferences. Experiments on two baseline datasets show that our proposal can achieve higher accuracy (at least 1%) and relatively efficient communication than two popular baselines under different heterogeneous settings.