# PFedDST: Personalized Federated Learning with Decentralized Selection Training

arXiv: https://arxiv.org/abs/2502.07750 · HTML: https://mlsanity.com/paper/2502.07750/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [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)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [Stochastic Gradient Push for Distributed Deep Learning](https://mlsanity.com/paper/1811.10792/) (2018)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training](https://mlsanity.com/paper/2206.00187/) (2022)
- [FreezeOut: Accelerate Training by Progressively Freezing Layers](https://mlsanity.com/paper/1706.04983/) (2017)
- [Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques](https://mlsanity.com/paper/2102.09743/) (2021)
- [Decentralized Directed Collaboration for Personalized Federated Learning](https://mlsanity.com/paper/2405.17876/) (2024)
- [HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2406.17697/) (2024)
- [TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection](https://mlsanity.com/paper/2501.14302/) (2025)
- [Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching](https://mlsanity.com/paper/2405.03782/) (2024)
- [Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach](https://mlsanity.com/paper/1905.02703/) (2019)
- [NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2303.17448/) (2023)
- [Enhanced Audit Bit Based Distributed Bayesian Detection in the Presence of Strategic Attacks](https://mlsanity.com/paper/2109.13325/) (2021)
- [Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks](https://mlsanity.com/paper/2207.08870/) (2022)
- [Distributed Quantized Detection of Sparse Signals Under Byzantine Attacks](https://mlsanity.com/paper/2304.14467/) (2023)
- [Measuring Heterogeneity in Machine Learning with Distributed Energy Distance](https://mlsanity.com/paper/2501.16174/) (2025)

## Built on by (most cited first, 4 of 4)

- [Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation](https://mlsanity.com/paper/2605.26382/) (2026)
- [E2CB2former: Effecitve and Explainable Transformer for CB2 Receptor Ligand Activity Prediction](https://mlsanity.com/paper/2502.12186/) (2025)
- [Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT](https://mlsanity.com/paper/2512.19131/) (2025)
- [SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning](https://mlsanity.com/paper/2609.23773/) (2026)

## Abstract (arXiv)

Distributed Learning (DL) enables the training of machine learning models across multiple devices, yet it faces challenges like non-IID data distributions and device capability disparities, which can impede training efficiency. Communication bottlenecks further complicate traditional Federated Learning (FL) setups. To mitigate these issues, we introduce the Personalized Federated Learning with Decentralized Selection Training (PFedDST) framework. PFedDST enhances model training by allowing devices to strategically evaluate and select peers based on a comprehensive communication score. This score integrates loss, task similarity, and selection frequency, ensuring optimal peer connections. This selection strategy is tailored to increase local personalization and promote beneficial peer collaborations to strengthen the stability and efficiency of the training process. Our experiments demonstrate that PFedDST not only enhances model accuracy but also accelerates convergence. This approach outperforms state-of-the-art methods in handling data heterogeneity, delivering both faster and more effective training in diverse and decentralized systems.