arxiv_ml 95% Match Research Paper AI Researchers,Machine Learning Engineers,Privacy Experts,Developers working with decentralized data 3 weeks ago

FedMMKT:Co-Enhancing a Server Text-to-Image Model and Client Task Models in Multi-Modal Federated Learning

large-language-models › multimodal-llms

📄 Abstract

Abstract: Text-to-Image (T2I) models have demonstrated their versatility in a wide range of applications. However, adaptation of T2I models to specialized tasks is often limited by the availability of task-specific data due to privacy concerns. On the other hand, harnessing the power of rich multimodal data from modern mobile systems and IoT infrastructures presents a great opportunity. This paper introduces Federated Multi-modal Knowledge Transfer (FedMMKT), a novel framework that enables co-enhancement of a server T2I model and client task-specific models using decentralized multimodal data without compromising data privacy.

Authors (7)

Ningxin He

Yang Liu

Wei Sun

Xiaozhou Ye

Ye Ouyang

Tiegang Gao

+1 more

Submitted

October 14, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Introduces Federated Multi-modal Knowledge Transfer (FedMMKT), a novel framework for co-enhancing a server Text-to-Image (T2I) model and client task-specific models using decentralized multimodal data without compromising privacy. It enables adaptation of T2I models to specialized tasks where data is scarce or private.

Business Value

Allows businesses to leverage sensitive, decentralized user data (e.g., from mobile devices) to create personalized and specialized AI services without centralizing raw data, enhancing privacy and utility.

Paper Metadata

Innovation Type

Federated Learning Framework

Deployment Feasibility

Moderate. Federated learning introduces complexities in communication, synchronization, and privacy guarantees. Practical deployment depends on the robustness of the FedMMKT framework.

Limitations Addressed

Limited availability of task-specific data for T2I model adaptation due to privacy concerns, and the challenge of harnessing rich multimodal data from decentralized sources.

Performance Gains

Enables adaptation of T2I models to specialized tasks while preserving data privacy, leading to improved performance on those tasks.

Technical Tags

Federated LearningText-to-ImageMulti-modalKnowledge TransferPrivacy PreservationDecentralized DataServer ModelClient ModelsCo-enhancementTask Adaptation

Research Topics

Federated LearningMulti-modal AIPrivacy-Preserving Machine LearningTransfer LearningText-to-Image GenerationDecentralized Systems

Methods & Architectures

Federated Multi-modal Knowledge Transfer (FedMMKT)Co-enhancementKnowledge Transfer Mechanisms Text-to-Image ModelTask-specific Models

Applications & Tasks

Mobile AI IoT Personalized Content Generation Healthcare Adapting T2I models to specialized tasks with limited dataLeveraging decentralized multimodal data privatelyCo-developing server and client models Text-to-Image GenerationTask-specific Image GenerationMulti-modal Learning

Related Fields

Distributed SystemsPrivacy EngineeringComputer Vision

Keywords

Federated LearningText-to-ImageMulti-modal AIKnowledge TransferPrivacyDecentralized DataServer-Client ModelsTask AdaptationMobile AIIoTGenerative AI

Academic Context

#Federated Learning#Multi-modal AI#Privacy-Preserving Machine Learning#Transfer Learning#Text-to-Image Generation#Decentralized Systems

Commercial Potential

Potential Products

Personalized image generation servicesPrivacy-preserving AI tools for mobile applicationsCustomizable content creation platforms

Target Industries

TechnologyMobileAdvertisingMediaHealthcare

Use Case Examples

Generating personalized images for users based on their private data on mobile devicesAdapting a general T2I model to generate medical images for specific diagnostic tasks without sharing patient dataCreating customized marketing visuals using decentralized user preferences

Competitive Edge

Addresses the critical intersection of multi-modal learning, federated learning, and privacy, offering a unique solution for decentralized model adaptation.

Market Opportunity

Large and growing market for personalized AI and privacy-preserving technologies.

Revenue Models

SaaS platformslicensing of the FedMMKT framework.

Resource Requirements

Compute Needs

High for server model training, moderate for client updates.

Data Requirements

Decentralized multimodal datasets (text and images).

Deployment Constraints

Requires robust communication infrastructure and careful management of federated learning protocols.

Scalability

Scalability depends on the number of clients and the complexity of the models.

Regulatory Considerations

GDPRCCPAData Privacy Regulations

Production Readiness

Maturity Level

Research

Time to Market

3-5 years

Patent Potential

Moderate

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