Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications (2408.04680v2)

Abstract: The ability of LLMs to transform, interpret, and comprehend vast quantities of heterogeneous data presents a significant opportunity to enhance data-driven care delivery. However, the sensitive nature of protected health information (PHI) raises valid concerns about data privacy and trust in remote LLM platforms. In addition, the cost associated with cloud-based AI services continues to impede widespread adoption. To address these challenges, we propose a shift in the LLM execution environment from opaque, centralized cloud providers to a decentralized and dynamic fog computing architecture. By executing open-weight LLMs in more trusted environments, such as the user's edge device or a fog layer within a local network, we aim to mitigate the privacy, trust, and financial challenges associated with cloud-based LLMs. We further present SpeziLLM, an open-source framework designed to facilitate rapid and seamless leveraging of different LLM execution layers and lowering barriers to LLM integration in digital health applications. We demonstrate SpeziLLM's broad applicability across six digital health applications, showcasing its versatility in various healthcare settings.

• The paper proposes a dynamic fog computing architecture that optimizes LLM execution in medical applications by enhancing privacy and cost-efficiency.
• It introduces SpeziLLM, an open-source framework that seamlessly deploys LLMs across edge, fog, and cloud layers to meet varying computational needs.
• Evaluation results highlight improved LLM integration in healthcare, with positive user feedback despite challenges in error handling and the learning curve.

Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications: An Expert Overview

The paper "Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications" addresses the need for a decentralized and dynamic fog computing architecture aimed at improving the execution of LLMs in sensitive digital health environments.

Introduction and Challenges

Healthcare data, particularly electronic health records (EHRs) and data from wearable devices, presents both an opportunity and a challenge. The vast amount of data can significantly improve patient outcomes if it is efficiently leveraged, yet the sensitive nature of this data raises privacy, trust, and regulatory concerns when processed by remote cloud services. Additionally, the high costs associated with cloud-based AI services are a barrier to widespread adoption. The paper proposes a shift from centralized cloud providers to a fog computing architecture, which ensures LLM execution closer to the user, like on edge devices or within a local network, to mitigate these issues.

Proposed Architecture

The proposed architecture focuses on a three-layer fog computing framework comprising edge, fog, and cloud layers. This is aimed at creating a decentralized system where LLM inference tasks can be dynamically dispatched based on privacy, trust, and computational capabilities. Such an architecture enhances efficiency, security, and cost-effectiveness while maintaining robust computational capabilities.

1. Edge Layer: This includes low-power IoT devices and end-user devices located at the network's edge. Tasks suited for these devices are expected to have minimal computational requirements.
2. Fog Layer: Positioned between the edge and the cloud, this layer uses powerful local computational resources to execute more demanding inference tasks. This setup offers a more trusted environment compared to distant cloud servers.
3. Cloud Layer: This involves centralized cloud infrastructure, which, although computationally powerful, poses privacy and financial concerns for sensitive data handling.

SpeziLLM: The Framework

To simplify the creation and deployment of applications leveraging this architecture, the authors present SpeziLLM, an open-source framework designed using the Swift programming language. SpeziLLM provides a uniform interface for LLM interactions and dynamically adjusts execution environments between edge, fog, and cloud layers. It supports major Apple operating systems and leverages Apple's hardware and software ecosystem, thereby optimizing LLM execution.

Contribution to Digital Health Applications

The framework's utility is demonstrated through six distinct digital health applications. These use cases include LLMonFHIR, which interacts with FHIR records via LLM capabilities; HealthGPT, which enables natural language querying of Apple Health data; and OwnYourData, aimed at increasing diversity in cancer clinical trials through LLM and FHIR EHR-based patient/paper matching. Each application leverages SpeziLLM's capabilities to handle different layers of computation according to the complexity of tasks and privacy considerations.

Key Findings

The evaluation of SpeziLLM's performance includes a survey of Computer Science students who used the framework in their projects. The feedback indicates that the framework significantly eased LLM integration, providing a positive user experience. However, there were noted challenges in managing the learning curve and error handling mechanisms.

Implications and Future Work

The adoption of a fog computing architecture for LLM execution in digital health applications holds significant implications. By bringing computation closer to the data source, SpeziLLM enhances privacy, trust, and cost-efficiency. The potential for developments in AI, particularly regarding efficient LLM execution on resource-constrained edge devices, could further improve the performance and applicability of this architecture.

Future research should focus on optimizing LLMs for local execution, improving memory management, and lowering the computational costs to make the technology accessible to a broader range of healthcare providers. Continued innovation in this domain can significantly enhance digital health applications, making healthcare delivery more efficient, secure, and patient-centric.

Conclusion

This paper demonstrates the viability of using a dynamic fog computing architecture to address the privacy, trust, and cost challenges associated with LLM execution in digital health applications. The developed framework, SpeziLLM, provides a versatile and efficient solution for deploying LLMs across different computational environments. The ongoing advancements in hardware and software optimizations for LLMs promise further enhancements in the capabilities and resilience of this approach.

The research underlines a significant step towards decentralizing AI computations, extending the benefits of advanced LLMs to sensitive domains like healthcare, where trust and privacy are paramount.