# pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

arXiv: https://arxiv.org/abs/2310.13283 · HTML: https://mlsanity.com/paper/2310.13283/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 28 of 28)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [Think Locally, Act Globally: Federated Learning with Local and Global Representations](https://mlsanity.com/paper/2001.01523/) (2020)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout](https://mlsanity.com/paper/2102.13451/) (2021)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data](https://mlsanity.com/paper/2008.06180/) (2020)
- [Parameterized Knowledge Transfer for Personalized Federated Learning](https://mlsanity.com/paper/2111.02862/) (2021)
- [FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction](https://mlsanity.com/paper/2212.01548/) (2022)
- [Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer](https://mlsanity.com/paper/1912.11279/) (2019)
- [Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning](https://mlsanity.com/paper/2204.12703/) (2022)
- [FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning](https://mlsanity.com/paper/2102.02514/) (2021)
- [FedGH: Heterogeneous Federated Learning with Generalized Global Header](https://mlsanity.com/paper/2303.13137/) (2023)
- [FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion](https://mlsanity.com/paper/2110.11027/) (2021)
- [Fed2: Feature-Aligned Federated Learning](https://mlsanity.com/paper/2111.14248/) (2021)
- [Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data](https://mlsanity.com/paper/1907.02745/) (2019)
- [FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models](https://mlsanity.com/paper/2109.03775/) (2021)
- [FedMatch: Federated Learning Over Heterogeneous Question Answering Data](https://mlsanity.com/paper/2108.05069/) (2021)
- [FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks](https://mlsanity.com/paper/2210.14226/) (2022)

## Built on by (most cited first, 30 of 40)

- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [Low-Rank Adaptation for Foundation Models: A Comprehensive Review](https://mlsanity.com/paper/2501.00365/) (2024)
- [A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning](https://mlsanity.com/paper/2504.21099/) (2025)
- [FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning](https://mlsanity.com/paper/2404.15182/) (2024)
- [LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning](https://mlsanity.com/paper/2505.21289/) (2025)
- [Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures](https://mlsanity.com/paper/2411.19128/) (2024)
- [Federated Continual Learning for Edge-AI: A Comprehensive Survey](https://mlsanity.com/paper/2411.13740/) (2024)
- [FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2506.02961/) (2025)
- [Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs](https://mlsanity.com/paper/2502.05087/) (2025)
- [SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning](https://mlsanity.com/paper/2405.09394/) (2024)
- [ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning](https://mlsanity.com/paper/2511.18291/) (2025)
- [FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation](https://mlsanity.com/paper/2505.18494/) (2025)
- [Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Models](https://mlsanity.com/paper/2412.04650/) (2024)
- [Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environments](https://mlsanity.com/paper/2510.03284/) (2025)
- [FedMPDD: Communication-Efficient Federated Learning with Privacy Preservation Attributes via Projected Directional Derivative](https://mlsanity.com/paper/2512.20814/) (2025)
- [Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning](https://mlsanity.com/paper/2608.01556/) (2026)
- [Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency](https://mlsanity.com/paper/2503.07552/) (2025)
- [FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA](https://mlsanity.com/paper/2506.01194/) (2025)
- [FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models](https://mlsanity.com/paper/2506.14824/) (2025)
- [FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA](https://mlsanity.com/paper/2508.00873/) (2025)
- [Communication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptation](https://mlsanity.com/paper/2509.26399/) (2025)
- [Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2605.05769/) (2026)
- [UnifiedFL: A Dynamic Unified Learning Framework for Equitable Federation](https://mlsanity.com/paper/2510.26350/) (2025)
- [Stabilizing Decentralized Federated Fine-Tuning via Topology-Aware Alternating LoRA](https://mlsanity.com/paper/2602.00451/) (2026)
- [Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm](https://mlsanity.com/paper/2602.11543/) (2026)
- [FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Client](https://mlsanity.com/paper/2602.12014/) (2026)
- [SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs](https://mlsanity.com/paper/2602.13529/) (2026)
- [HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learning](https://mlsanity.com/paper/2603.02785/) (2026)
- [FedSDR: Federated Self-Distillation with Rectification](https://mlsanity.com/paper/2605.18028/) (2026)
- [Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models](https://mlsanity.com/paper/2606.06154/) (2026)

## Abstract (arXiv)

Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data. In practice, FL often faces statistical, system, and model heterogeneities, which inspires the field of Model-Heterogeneous Personalized Federated Learning (MHPFL). With the increased interest in adopting large language models (LLMs) in FL, the existing MHPFL methods cannot achieve acceptable computational and communication costs, while maintaining satisfactory model performance. To bridge this gap, we propose a novel and efficient model-heterogeneous personalized Federated learning framework based on LoRA tuning (pFedLoRA). Inspired by the popular LoRA method for fine-tuning pre-trained LLMs with a low-rank model (a.k.a., an adapter), we design a homogeneous small adapter to facilitate federated client's heterogeneous local model training with our proposed iterative training for global-local knowledge exchange. The homogeneous small local adapters are aggregated on the FL server to generate a global adapter. We theoretically prove the convergence of pFedLoRA. Extensive experiments on two benchmark datasets demonstrate that pFedLoRA outperforms six state-of-the-art baselines, beating the best method by 1.35% in test accuracy, 11.81 times computation overhead reduction and 7.41 times communication cost saving.