# Dual-Personalizing Adapter for Federated Foundation Models

arXiv: https://arxiv.org/abs/2403.19211 · HTML: https://mlsanity.com/paper/2403.19211/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 45 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 30 of 36)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2110.04366/) (2021)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://mlsanity.com/paper/2306.04751/) (2023)
- [LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2304.01933/) (2023)
- [Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment](https://mlsanity.com/paper/2312.12148/) (2023)
- [Towards Building the Federated GPT: Federated Instruction Tuning](https://mlsanity.com/paper/2305.05644/) (2023)
- [Federated Continual Learning with Weighted Inter-client Transfer](https://mlsanity.com/paper/2003.03196/) (2020)
- [FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://mlsanity.com/paper/2309.00363/) (2023)
- [KronA: Parameter Efficient Tuning with Kronecker Adapter](https://mlsanity.com/paper/2212.10650/) (2022)
- [Federated Class-Incremental Learning](https://mlsanity.com/paper/2203.11473/) (2022)
- [SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models](https://mlsanity.com/paper/2308.06522/) (2023)
- [When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions](https://mlsanity.com/paper/2306.15546/) (2023)
- [When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods](https://mlsanity.com/paper/2212.10025/) (2022)
- [Out of Distribution Generalization in Machine Learning](https://mlsanity.com/paper/2103.02667/) (2021)
- [FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning](https://mlsanity.com/paper/2308.12305/) (2023)
- [Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models](https://mlsanity.com/paper/2305.11414/) (2023)
- [Test-Time Robust Personalization for Federated Learning](https://mlsanity.com/paper/2205.10920/) (2022)
- [Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning](https://mlsanity.com/paper/2307.09619/) (2023)

## Built on by (most cited first, 30 of 55)

- [Selective Aggregation for Low-Rank Adaptation in Federated Learning](https://mlsanity.com/paper/2410.01463/) (2024)
- [VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing](https://mlsanity.com/paper/2502.17258/) (2025)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning](https://mlsanity.com/paper/2504.21099/) (2025)
- [Synergizing Foundation Models and Federated Learning: A Survey](https://mlsanity.com/paper/2406.12844/) (2024)
- [FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA](https://mlsanity.com/paper/2503.11880/) (2025)
- [Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures](https://mlsanity.com/paper/2411.19128/) (2024)
- [Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach](https://mlsanity.com/paper/2408.08931/) (2024)
- [Vision Foundation Models in Medical Image Analysis: Advances and Challenges](https://mlsanity.com/paper/2502.14584/) (2025)
- [Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks](https://mlsanity.com/paper/2503.11514/) (2025)
- [Federated Low-Rank Adaptation for Foundation Models: A Survey](https://mlsanity.com/paper/2505.13502/) (2025)
- [Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning](https://mlsanity.com/paper/2509.15087/) (2025)
- [FedSRD: Sparsify-Reconstruct-Decompose for Communication-Efficient Federated Large Language Models Fine-Tuning](https://mlsanity.com/paper/2510.04601/) (2025)
- [FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment](https://mlsanity.com/paper/2602.17095/) (2026)
- [FedMerge: Federated Personalization via Model Merging](https://mlsanity.com/paper/2504.06768/) (2025)
- [A Survey on Foundation Models for Personalized Federated Intelligence](https://mlsanity.com/paper/2505.06907/) (2025)
- [Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR](https://mlsanity.com/paper/2506.05683/) (2025)
- [Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach](https://mlsanity.com/paper/2604.21197/) (2026)
- [Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation](https://mlsanity.com/paper/2503.23869/) (2025)
- [Federated Adapter on Foundation Models: An Out-Of-Distribution Approach](https://mlsanity.com/paper/2505.01075/) (2025)
- [FLEx: Personalized Federated Learning for Mixture-of-Experts LLMs via Expert Grafting](https://mlsanity.com/paper/2506.00965/) (2025)
- [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)
- [FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning](https://mlsanity.com/paper/2603.13282/) (2026)
- [Sequential Compression Layers for Efficient Federated Learning in Foundational Models](https://mlsanity.com/paper/2412.07021/) (2024)
- [FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models](https://mlsanity.com/paper/2506.14824/) (2025)
- [FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation](https://mlsanity.com/paper/2508.20295/) (2025)
- [Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights](https://mlsanity.com/paper/2509.05142/) (2025)
- [FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints](https://mlsanity.com/paper/2509.06984/) (2025)
- [Communication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptation](https://mlsanity.com/paper/2509.26399/) (2025)

## Abstract (arXiv)

Recently, foundation models, particularly large language models (LLMs), have demonstrated an impressive ability to adapt to various tasks by fine-tuning diverse instruction data. Notably, federated foundation models (FedFM) emerge as a privacy preservation method to fine-tune models collaboratively under federated learning (FL) settings by leveraging many distributed datasets with non-IID data. To alleviate communication and computation overhead, parameter-efficient methods are introduced for efficiency, and some research adapted personalization methods to FedFM for better user preferences alignment. However, a critical gap in existing research is the neglect of test-time distribution shifts in real-world applications, and conventional methods for test-time distribution shifts in personalized FL are less effective for FedFM due to their failure to adapt to complex distribution shift scenarios and the requirement to train all parameters. To bridge this gap, we refine the setting in FedFM, termed test-time personalization, which aims to learn personalized federated foundation models on clients while effectively handling test-time distribution shifts simultaneously. To address challenges in this setting, we explore a simple yet effective solution, a Federated Dual-Personalizing Adapter (FedDPA) architecture. By co-working with a foundation model, a global adapter and a local adapter jointly tackle the test-time distribution shifts and client-specific personalization. Additionally, we introduce an instance-wise dynamic weighting mechanism that dynamically integrates the global and local adapters for each test instance during inference, facilitating effective test-time personalization. The effectiveness of the proposed method has been evaluated on benchmark datasets across different NLP tasks.