# Personalized federated prototype learning in mixed heterogeneous data scenarios

arXiv: https://arxiv.org/abs/2510.03726 · HTML: https://mlsanity.com/paper/2510.03726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Federated Learning of a Mixture of Global and Local Models](https://mlsanity.com/paper/2002.05516/) (2020)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [Federated Learning on Non-IID Graphs via Structural Knowledge Sharing](https://mlsanity.com/paper/2211.13009/) (2022)
- [FedProc: Prototypical Contrastive Federated Learning on Non-IID data](https://mlsanity.com/paper/2109.12273/) (2021)
- [Tackling Data Heterogeneity in Federated Learning with Class Prototypes](https://mlsanity.com/paper/2212.02758/) (2022)
- [Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking](https://mlsanity.com/paper/2412.15691/) (2024)
- [Effects of Degradations on Deep Neural Network Architectures](https://mlsanity.com/paper/1807.10108/) (2018)
- [FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation](https://mlsanity.com/paper/2205.14900/) (2022)
- [FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers](https://mlsanity.com/paper/2203.00219/) (2022)
- [Adaptive Perception for Unified Visual Multi-modal Object Tracking](https://mlsanity.com/paper/2502.06583/) (2025)
- [Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos](https://mlsanity.com/paper/2504.14921/) (2025)
- [SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding](https://mlsanity.com/paper/2502.16786/) (2025)
- [Learning Multi-scale Spatial-frequency Features for Image Denoising](https://mlsanity.com/paper/2506.16307/) (2025)
- [An Effective End-to-End Solution for Multimodal Action Recognition](https://mlsanity.com/paper/2506.09345/) (2025)
- [Explicit Context Reasoning with Supervision for Visual Tracking](https://mlsanity.com/paper/2507.16191/) (2025)
- [When Color-Space Decoupling Meets Diffusion for Adverse-Weather Image Restoration](https://mlsanity.com/paper/2509.17024/) (2025)
- [Personalized federated learning based on feature fusion](https://mlsanity.com/paper/2406.16583/) (2024)

## Abstract (arXiv)

Federated learning has received significant attention for its ability to simultaneously protect customer privacy and leverage distributed data from multiple devices for model training. However, conventional approaches often focus on isolated heterogeneous scenarios, resulting in skewed feature distributions or label distributions. Meanwhile, data heterogeneity is actually a key factor in improving model performance. To address this issue, we propose a new approach called PFPL in mixed heterogeneous scenarios. The method provides richer domain knowledge and unbiased convergence targets by constructing personalized, unbiased prototypes for each client. Moreover, in the local update phase, we introduce consistent regularization to align local instances with their personalized prototypes, which significantly improves the convergence of the loss function. Experimental results on Digits and Office Caltech datasets validate the effectiveness of our approach and successfully reduce the communication cost.