Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs
arXiv:2501.19389v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on resource-constrained clients remains a challenging problem. Recent works have fused low-rank adaptation (LoRA) techniques with federated fine-tuning to mitigate challenges associated with client model sizes and data scarcity. Still, the heterogeneity of resources remains a critical bottleneck: while higher-rank modules generally enhance performance, varying client capabilities constrain LoRA's feasible rank range. Existing approaches attempting to resolve this issue either lack analytical justificat
The proliferation of Large Language Models (LLMs) and the increasing need for their adaptation to diverse, resource-constrained environments drives immediate research into efficient fine-tuning methods.
This development addresses critical bottlenecks in federated fine-tuning of LLMs, enabling more flexible and efficient deployment across various client capabilities, which is crucial for pervasive AI adoption.
The ability to fine-tune LLMs effectively on heterogeneous, resource-limited clients will accelerate the decentralization and customization of advanced AI, overcoming current scale and resource constraints.
- · Edge device manufacturers
- · Developers of federated learning platforms
- · Businesses with distributed data
- · Centralized cloud AI providers (marginal)
More LLMs can be fine-tuned and deployed on a wider range of devices without requiring significant local computational resources.
This leads to increased personalization and localization of AI services, enhancing their relevance and efficiency for end-users.
The widespread distribution of customized LLMs could accelerate the development of more sophisticated, domain-specific AI applications and reduce dependency on monolithic cloud-based models.
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