SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients
arXiv:2601.11219v3 Announce Type: replace-cross Abstract: Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) are widely adopted to reduce communication and memory costs. However, practical deployments often exhibit rank and data heterogeneity: clients operate under different low-rank budgets and data distributions, making direct aggregation of LoRA updates biased and unstable. Existing approaches either enforce
The increasing adoption of federated learning for large language models highlights the immediate need for more efficient and privacy-preserving fine-tuning methods, especially with heterogeneous client environments.
This development addresses critical challenges in scaling and securely deploying large AI models in distributed settings, directly impacting the feasibility of privacy-preserving AI applications.
The ability to stably and effectively aggregate LoRA updates from diverse clients removes a significant bottleneck for federated learning in real-world, heterogeneous large language model deployments.
- · AI developers focused on privacy
- · Organizations with distributed data
- · Federated learning platforms
- · Centralized model training approaches
- · Inefficient communication protocols
Improved efficiency and privacy in fine-tuning large language models across diverse client devices or organizations.
Accelerated adoption of federated learning for enterprise AI applications demanding privacy and data locality.
Enhanced development of sovereign AI capabilities through secure, distributed model adaptation without central data aggregation.
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Read at arXiv cs.AI