# Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edge

arXiv: https://arxiv.org/abs/2503.21412 · HTML: https://mlsanity.com/paper/2503.21412/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services](https://mlsanity.com/paper/2303.16129/) (2023)
- [SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2407.00952/) (2024)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration](https://mlsanity.com/paper/2312.11489/) (2023)
- [The Role of Federated Learning in a Wireless World with Foundation Models](https://mlsanity.com/paper/2310.04003/) (2023)

## Built on by (most cited first, 4 of 4)

- [Get Experience from Practice: LLM Agents with Record & Replay](https://mlsanity.com/paper/2505.17716/) (2025)
- [FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning](https://mlsanity.com/paper/2510.12078/) (2025)
- [LLM Agent for Hyper-Parameter Optimization](https://mlsanity.com/paper/2506.15167/) (2025)
- [Closing the Generalization Gap in Parameter-efficient Federated Edge Learning](https://mlsanity.com/paper/2511.23282/) (2025)

## Abstract (arXiv)

Large artificial intelligence (AI) models exhibit remarkable capabilities in various application scenarios, but deploying them at the network edge poses significant challenges due to issues such as data privacy, computational resources, and latency. In this paper, we explore federated fine-tuning and collaborative reasoning techniques to facilitate the implementation of large AI models in resource-constrained wireless networks. Firstly, promising applications of large AI models within specific domains are discussed. Subsequently, federated fine-tuning methods are proposed to adapt large AI models to specific tasks or environments at the network edge, effectively addressing the challenges associated with communication overhead and enhancing communication efficiency. These methodologies follow clustered, hierarchical, and asynchronous paradigms to effectively tackle privacy issues and eliminate data silos. Furthermore, to enhance operational efficiency and reduce latency, efficient frameworks for model collaborative reasoning are developed, which include decentralized horizontal collaboration, cloud-edge-end vertical collaboration, and multi-access collaboration. Next, simulation results demonstrate the effectiveness of our proposed methods in reducing the fine-tuning loss of large AI models across various downstream tasks. Finally, several open challenges and research opportunities are outlined.