FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models (2506.09199v1)

Abstract: Integrating Low-Rank Adaptation (LoRA) into federated learning offers a promising solution for parameter-efficient fine-tuning of LLMs without sharing local data. However, several methods designed for federated LoRA present significant challenges in balancing communication efficiency, model accuracy, and computational cost, particularly among heterogeneous clients. These methods either rely on simplistic averaging of local adapters, which introduces aggregation noise, require transmitting large stacked local adapters, leading to poor communication efficiency, or necessitate reconstructing memory-dense global weight-update matrix and performing computationally expensive decomposition to design client-specific low-rank adapters. In this work, we propose FLoRIST, a federated fine-tuning framework that achieves mathematically accurate aggregation without incurring high communication or computational overhead. Instead of constructing the full global weight-update matrix at the server, FLoRIST employs an efficient decomposition pipeline by performing singular value decomposition on stacked local adapters separately. This approach operates within a compact intermediate space to represent the accumulated information from local LoRAs. We introduce tunable singular value thresholding for server-side optimal rank selection to construct a pair of global low-rank adapters shared by all clients. Extensive empirical evaluations across multiple datasets and LLMs demonstrate that FLoRIST consistently strikes the best balance between superior communication efficiency and competitive performance in both homogeneous and heterogeneous setups.

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of LLMs

The paper "FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of LLMs" presents a novel approach integrating Low-Rank Adaptation (LoRA) into federated learning to improve the fine-tuning process of LLMs without local data sharing. The authors focus on resolving pivotal challenges associated with federated LoRA, such as balancing communication efficiency, computational costs, and model accuracy, especially in environments with heterogeneous clients.

The traditional federated LoRA methods encounter several drawbacks: simplistic averaging of local adapters that introduce noise, poor communication efficiency due to large data transmission demands, and computationally intensive processes for client-specific adapter reconstruction. These disadvantages hinder the performance and practicality of federated learning in real-world applications.

FLoRIST proposes an advanced strategy leveraging Singular Value Decomposition (SVD) on stacked local adapters, circumventing the necessity of a full global weight-update matrix at the server. This approach efficiently aggregates local information using a compact intermediate representation, which enhances both communication efficiency and computational performance. Tunable singular value thresholding is implemented to optimize rank selection on the server-side, ensuring the construction of unified global low-rank adapters across all clients.

The empirical evaluations demonstrate that FLoRIST maintains an optimal balance between communication efficiency and competitive performance across diverse datasets and LLM architectures, in both homogeneous and heterogeneous settings. This evidence supports FLoRIST as a viable method for federated fine-tuning, offering substantial improvements over existing strategies.

Implications and Future Directions

The research positioned by FLoRIST has significant implications for both theoretical and practical advancements in federated learning with LLMs. By reducing the communication burden and computational demands, FLoRIST sets the groundwork for deploying LLMs more feasibly in decentralized and privacy-sensitive environments.

From a theoretical perspective, the paper contributes to the understanding of efficient adapter aggregation methods in federated settings, inspiring further exploration into decomposition and aggregation techniques within distributed machine learning paradigms. Practically, FLoRIST enables more scalable and resource-efficient operations in federated learning infrastructures, potentially advancing applications across mobile, edge, and IoT devices.

Looking ahead, the methodology proposed in FLoRIST opens pathways for research into adapting singular value thresholding for even larger-scale models and more complex federated setups involving various client constraints and diverse data characteristics. Further exploration into hybrid models integrating this approach with other machine learning techniques could enhance the robustness and application efficacy of federated learning systems in increasingly demanding environments.