FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
arXiv:2506.09199v2 Announce Type: replace Abstract: Integrating Low-Rank Adaptation (LoRA) into federated learning offers a promising solution for parameter-efficient fine-tuning of Large Language Models (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,
The increasing scale of LLMs combined with the need for data privacy and distributed computation drives innovation in federated learning and efficient fine-tuning techniques.
Efficient and accurate federated fine-tuning enables LLMs to be adapted to diverse client needs without centralizing sensitive data, addressing critical privacy and scalability challenges.
New methods like FLoRIST improve upon existing federated LoRA techniques, potentially leading to more widespread and effective adoption of privacy-preserving LLM customization.
- · AI developers
- · Enterprises with sensitive data
- · Federated learning platforms
- · Edge AI providers
- · Centralized data processing models
- · Methods requiring high communication bandwidth
- · Inflexible LLM deployments
More secure and distributed fine-tuning of large AI models becomes practical for a wider range of applications and clients.
This could accelerate the adoption of personalized AI services in regulated industries like healthcare and finance, where data privacy is paramount.
Improved efficiency in federated learning might lessen the demand for massive centralized compute, potentially decentralizing parts of the AI training infrastructure.
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Read at arXiv cs.LG