arXiv:2605.23913v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) commonly rely on LoRA for domain adaptation, yet domain data are distributed across multiple edge devices and cannot be uploaded due to privacy constraints. This raises a fundamental question: how can knowledge from multiple private edges be integrated into a cloud LLM for cross-domain problem solving? A natural solution is to train LoRA adapters locally and fuse them in the cloud; however, existing pipelines rely on unrealistic assumptions that edge devices can host cloud-scale LLMs and are evaluated m
The rapid deployment of LLMs and the increasing focus on data privacy and edge computing capabilities necessitate distributed training and adaptation methods.
This research addresses a critical challenge in scaling AI adoption, enabling organizations to leverage private, distributed data for powerful AI models without compromising security or data sovereignty.
The ability to fuse LoRA adapters from multiple edge devices could decentralize AI training, allowing for more tailored and privacy-preserving AI applications in various domains.
- · Edge computing providers
- · Privacy-focused AI applications
- · Cloud providers offering LoRA fusion services
- · Industries with sensitive data (e.g., healthcare, finance)
- · Centralized AI training paradigms dependent on monolithic datasets
- · Organizations unable to adapt to distributed AI workflows
Improved performance of cloud LLMs for domain-specific tasks using federated edge data.
Increased adoption of edge AI and federated learning paradigms due to enhanced utility and data privacy.
The development of new AI business models centered around secure, distributed intelligence across diverse data environments.
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