# Edge-Assisted Collaborative Fine-Tuning for Multi-User Personalized Artificial Intelligence Generated Content (AIGC)

arXiv: https://arxiv.org/abs/2508.04745 · HTML: https://mlsanity.com/paper/2508.04745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 13 of 13)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations](https://mlsanity.com/paper/2409.05976/) (2024)
- [Implicit Style-Content Separation using B-LoRA](https://mlsanity.com/paper/2403.14572/) (2024)
- [DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2309.06933/) (2023)
- [Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks](https://mlsanity.com/paper/2304.03446/) (2023)
- [FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models](https://mlsanity.com/paper/2410.04810/) (2024)
- [Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models](https://mlsanity.com/paper/2408.06646/) (2024)
- [The Role of Federated Learning in a Wireless World with Foundation Models](https://mlsanity.com/paper/2310.04003/) (2023)
- [EC-Diff: Fast and High-Quality Edge-Cloud Collaborative Inference for Diffusion Models](https://mlsanity.com/paper/2507.11980/) (2025)
- [Efficient Multi-user Offloading of Personalized Diffusion Models: A DRL-Convex Hybrid Solution](https://mlsanity.com/paper/2411.15781/) (2024)

## Built on by (most cited first, 1 of 1)

- [Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges](https://mlsanity.com/paper/2609.04778/) (2026)

## Abstract (arXiv)

Diffusion models (DMs) have emerged as powerful tools for high-quality content generation, yet their intensive computational requirements for inference pose challenges for resource-constrained edge devices. Cloud-based solutions aid in computation but often fall short in addressing privacy risks, personalization efficiency, and communication costs in multi-user edge-AIGC scenarios. To bridge this gap, we first analyze existing edge-AIGC applications in personalized content synthesis, revealing their limitations in efficiency and scalability. We then propose a novel cluster-aware hierarchical federated aggregation framework. Based on parameter-efficient local fine-tuning via Low-Rank Adaptation (LoRA), the framework first clusters clients based on the similarity of their uploaded task requirements, followed by an intra-cluster aggregation for enhanced personalization at the server-side. Subsequently, an inter-cluster knowledge interaction paradigm is implemented to enable hybrid-style content generation across diverse clusters.Building upon federated learning (FL) collaboration, our framework simultaneously trains personalized models for individual users at the devices and a shared global model enhanced with multiple LoRA adapters on the server,enabling efficient edge inference; meanwhile, all prompts for clustering and inference are encoded prior to transmission, thereby further mitigating the risk of plaintext leakage. Our evaluations demonstrate that the framework achieves accelerated convergence while maintaining practical viability for scalable multi-user personalized AIGC services under edge constraints.