Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks, with fine-tuning playing a pivotal role in adapting them to specific downstream applications. Federated Learning (FL) offers a promising approach that enables collaborative model adaptation while ensuring data privacy, i.e., FedLLM. In this survey, we provide a systematic and thorough review of the integration of LLMs with FL. Specifically, we first trace the historical evolution of both LLMs and FL, while summarizing relevant prior surveys. We then present an in-depth analysis of the fundamental challenges encountered in deploying FedLLM. Following this, we conduct an extensive study of existing parameter-efficient fine-tuning (PEFT) methods and explore their applicability in FL. Furthermore, we introduce a comprehensive evaluation benchmark to rigorously assess FedLLM performance and discuss its diverse real-world applications across multiple domains. Finally, we identify critical open challenges and outline promising research directions to drive future advancements in FedLLM. We maintain an active \href{https://github.com/Clin0212/Awesome-Federated-LLM-Learning}{GitHub repository} tracking cutting-edge advancements. This survey serves as a foundational resource for researchers and practitioners, offering insights into the evolving landscape of federated fine-tuning for LLMs while guiding future innovations in privacy-preserving AI.

Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices severely bottlenecks the deployment of FL in real-world scenarios. Thus, a framework that can effectively break the memory wall while jointly taking into account the hardware and statistical heterogeneity in FL is urgently required. In this paper, we propose SmartSplit, a framework that effectively reduces the memory footprint on the device side while guaranteeing the training progress and model accuracy for heterogeneous FL through model splitting.Towards this end, SmartSplit employs a hierarchical structure to adaptively guide the overall training process. In each training round, the central manager, hosted on the server, dynamically selects the participating devices and sets the cutting layer by jointly considering the memory budget, training capacity, and data distribution of each device. The MEC manager, deployed within the edge server, proceeds to split the local model and perform training of the server-side portion. Meanwhile, it fine-tunes the splitting points based on the time-evolving statistical importance. The on-device manager, embedded inside each mobile device, continuously monitors the local training status while employing cost-aware checkpointing to match the runtime dynamic memory budget. Extensive experiments on representative datasets are conducted on both commercial off-the-shelf mobile device testbeds. The experimental results show that SmartSplit excels in FL training on highly memory-constrained mobile SoCs, offering up to a 94% peak latency reduction and 100-fold memory savings. It enhances accuracy performance by 1.49%-57.18% and adaptively adjusts to dynamic memory budgets through cost-aware recomputation.

This paper presents ProFL, a novel progressive FL framework to effectively break the memory wall. Specifically, ProFL divides the model into different blocks based on its original architecture. Instead of updating the full model in each training round, ProFL first trains the front blocks and safely freezes them after convergence. Training of the next block is then triggered. This process iterates until the training of the whole model is completed. In this way, the memory footprint is effectively reduced for feasible deployment on heterogeneous devices. In order to preserve the feature representation of each block, we decouple the whole training process into two stages: progressive model shrinking and progressive model growing. During the progressive model shrinking stage, we meticulously design corresponding output modules to assist each block in learning the expected feature representation and obtain the initialization parameters. Then, the obtained output modules are utilized in the corresponding progressive model growing stage. Additionally, to control the training pace for each block, a novel metric from the scalar perspective is proposed to assess the learning status of each block and determines when to trigger the training of the next one. Finally, we theoretically prove the convergence of ProFL and conduct extensive experiments on representative models and datasets to evaluate the effectiveness of ProFL. The results demonstrate that ProFL effectively reduces the peak memory footprint by up to 57.4% and improves model accuracy by up to 82.4%.