# FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning

arXiv: https://arxiv.org/abs/2404.02478 · HTML: https://mlsanity.com/paper/2404.02478/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 30 of 31)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Linear Mode Connectivity and the Lottery Ticket Hypothesis](https://mlsanity.com/paper/1912.05671/) (2019)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Think Locally, Act Globally: Federated Learning with Local and Global Representations](https://mlsanity.com/paper/2001.01523/) (2020)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [Surgical Fine-Tuning Improves Adaptation to Distribution Shifts](https://mlsanity.com/paper/2210.11466/) (2022)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)
- [Federated Learning with Partial Model Personalization](https://mlsanity.com/paper/2204.03809/) (2022)
- [Personalized Federated Learning with Feature Alignment and Classifier Collaboration](https://mlsanity.com/paper/2306.11867/) (2023)

## Built on by (most cited first, 27 of 27)

- [F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics](https://mlsanity.com/paper/2411.11912/) (2024)
- [Exploring Selective Layer Fine-Tuning in Federated Learning](https://mlsanity.com/paper/2408.15600/) (2024)
- [FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients](https://mlsanity.com/paper/2411.01825/) (2024)
- [pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup](https://mlsanity.com/paper/2501.11002/) (2025)
- [Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation](https://mlsanity.com/paper/2407.16139/) (2024)
- [Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning](https://mlsanity.com/paper/2412.07062/) (2024)
- [APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares](https://mlsanity.com/paper/2508.10732/) (2025)
- [TAP: Two-Stage Adaptive Personalization of Multi-Task and Multi-Modal Foundation Models in Federated Learning](https://mlsanity.com/paper/2509.26524/) (2025)
- [pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning](https://mlsanity.com/paper/2503.14925/) (2025)
- [Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift](https://mlsanity.com/paper/2412.15010/) (2024)
- [Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing](https://mlsanity.com/paper/2502.18123/) (2025)
- [FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation](https://mlsanity.com/paper/2503.15390/) (2025)
- [Federated Neural Architecture Search with Model-Agnostic Meta Learning](https://mlsanity.com/paper/2504.06457/) (2025)
- [Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity](https://mlsanity.com/paper/2506.00932/) (2025)
- [FedHiP: Heterogeneity-Invariant Personalized Federated Learning Through Closed-Form Solutions](https://mlsanity.com/paper/2508.04470/) (2025)
- [Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning](https://mlsanity.com/paper/2508.05157/) (2025)
- [Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection](https://mlsanity.com/paper/2508.16976/) (2025)
- [Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking](https://mlsanity.com/paper/2509.14603/) (2025)
- [Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation](https://mlsanity.com/paper/2509.25906/) (2025)
- [CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks](https://mlsanity.com/paper/2510.20219/) (2025)
- [A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning](https://mlsanity.com/paper/2511.12695/) (2025)
- [Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery](https://mlsanity.com/paper/2603.07774/) (2026)
- [HEART-PFL: Stable Personalized Federated Learning under Heterogeneity with Hierarchical Directional Alignment and Adversarial Knowledge Transfer](https://mlsanity.com/paper/2603.24209/) (2026)
- [FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling](https://mlsanity.com/paper/2604.16574/) (2026)
- [COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication](https://mlsanity.com/paper/2605.11165/) (2026)
- [Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning](https://mlsanity.com/paper/2605.17552/) (2026)
- [FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains](https://mlsanity.com/paper/2610.01510/) (2026)

## Abstract (arXiv)

Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions. Existing PFL methods usually decouple global updates in deep neural networks by performing personalization on particular layers (i.e. classifier heads) and global aggregation for the rest of the network. However, preselecting network layers for personalization may result in suboptimal storage of global knowledge. In this work, we propose FedSelect, a novel PFL algorithm inspired by the iterative subnetwork discovery procedure used for the Lottery Ticket Hypothesis. FedSelect incrementally expands subnetworks to personalize client parameters, concurrently conducting global aggregations on the remaining parameters. This approach enables the personalization of both client parameters and subnetwork structure during the training process. Finally, we show that FedSelect outperforms recent state-of-the-art PFL algorithms under challenging client data heterogeneity settings and demonstrates robustness to various real-world distributional shifts. Our code is available at https://github.com/lapisrocks/fedselect.