pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models

• URL: http://arxiv.org/abs/2507.05394v1
• Date: Mon, 07 Jul 2025 18:26:34 GMT
• Title: pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
• Authors: Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani,
• Abstract summary: pFedMMA is the first personalized federated learning framework that leverages multi-modal adapters for vision-language tasks.<n>We show that pFedMMA achieves state-of-the-art trade-offs between personalization and generalization, outperforming recent federated prompt tuning methods.
• Score: 14.75695352321115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) like CLIP have demonstrated remarkable generalization in zero- and few-shot settings, but adapting them efficiently to decentralized, heterogeneous data remains a challenge. While prompt tuning has emerged as a popular parameter-efficient approach in personalized federated learning, existing methods often sacrifice generalization in favor of personalization, struggling particularly on unseen classes or domains. In this work, we propose pFedMMA, the first personalized federated learning framework that leverages multi-modal adapters for vision-language tasks. Each adapter contains modality-specific up- and down-projection layers alongside a globally shared projection that aligns cross-modal features. Our asymmetric optimization strategy allows clients to locally adapt to personalized data distributions while collaboratively training the shared projection to improve global generalization. This design is also communication-efficient, as only the shared component is exchanged during rounds. Through extensive experiments across eleven datasets, including domain- and label-shift scenarios, we show that pFedMMA achieves state-of-the-art trade-offs between personalization and generalization, outperforming recent federated prompt tuning methods. The code is available at https://github.com/sajjad-ucsb/pFedMMA.

Related papers

• Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion [44.8670376715096]
  Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data.<n>We propose a personalized FL framework based on dual-prompt learning and cross fusion, termed pFedDC.
  arXiv  Detail & Related papers  (2025-06-26T10:59:14Z)
• Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations [4.008780119020479]
  Federated Learning aims to train models across decentralized clients or devices holding local data without the need for centralized data collection.<n>We introduce FedOT, a novel approach that leverages black-box foundation models.<n>FedOT mitigates gradient conflicts across diverse clients, preserves semantic integrity, and achieves robust performance even in the presence of substantial data.
  arXiv  Detail & Related papers  (2025-05-26T12:18:24Z)
• Not All Clients Are Equal: Personalized Federated Learning on Heterogeneous Multi-Modal Clients [52.14230635007546]
  Foundation models have shown remarkable capabilities across diverse multi-modal tasks, but their centralized training raises privacy concerns and induces high transmission costs.<n>For the growing demand for personalizing AI models for different user purposes, personalized federated learning (PFL) has emerged.<n>PFL allows each client to leverage the knowledge of other clients for further adaptation to individual user preferences, again without the need to share data.
  arXiv  Detail & Related papers  (2025-05-20T09:17:07Z)
• PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning [14.681194790227085]
  Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities.<n> Personalized federated learning addresses this issue by dividing the model into a globally shared part and a locally private part.<n>We propose PM-MoE architecture, which integrates a mixture of personalized modules and an energy-based personalized modules denoising.
  arXiv  Detail & Related papers  (2025-02-01T07:20:21Z)
• Personalized Federated Learning via Feature Distribution Adaptation [3.410799378893257]
  Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client. We propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions.
  arXiv  Detail & Related papers  (2024-11-01T03:03:52Z)
• FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization [19.328216705039527]
  FedMCP is a novel parameter-efficient fine-tuning method with model-contrastive personalization for FL. We show that FedMCP achieves substantial performance improvements over state-of-the-art FL fine-tuning approaches for PLMs.
  arXiv  Detail & Related papers  (2024-08-28T04:19:47Z)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning [49.72857433721424]
  Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. We present a novel algorithm, SGPT, that integrates Generalized FL (GFL) and Personalized FL (PFL) approaches by employing a unique combination of both shared and group-specific prompts.
  arXiv  Detail & Related papers  (2023-10-27T17:22:09Z)
• Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings [41.98633628526484]
  Mixture-of-Experts (MoEs) achieve scalability by dynamically activating subsets of their components.<n>Motivated by inference costs and data heterogeneity, we study how joint training of gating functions and experts can allocate domain-specific expertise.
  arXiv  Detail & Related papers  (2023-06-14T15:47:52Z)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer [60.70102634957392]
  Domain generalization (DG) has been a hot topic in image recognition, with a goal to train a general model that can perform well on unseen domains. In this paper, we propose a novel domain generalization method for image recognition through cross-client style transfer (CCST) without exchanging data samples. Our method outperforms recent SOTA DG methods on two DG benchmarks (PACS, OfficeHome) and a large-scale medical image dataset (Camelyon17) in the FL setting.
  arXiv  Detail & Related papers  (2022-10-03T13:15:55Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.