Edge-to-Cloud Swarm Coordination for precision oncology clinical workflows with embodied agent feedback loops

说明与用途

一种分布式边缘到云的群体协调架构，利用具身代理满足精准肿瘤学工作流中的延迟、隐私、多样性和突发性需求。

问题

Centralized cloud inference cannot meet the latency, privacy, heterogeneity, and burstiness constraints of precision oncology workflows, such as whole‑slide image analysis and real‑time radiomics.

结果

The embodied‑feedback swarm achieved nearly three times faster policy convergence than a centralized scheduler in a robotics simulator, and the architecture scales to real oncology workloads by respecting latency, privacy, heterogeneity, and burstiness constraints.

工作流

• 1. A task (e.g., tile a WSI region) is announced to the broker.
• 2. Each embodied agent in Tier 1 gathers its local state (compute headroom, thermal headroom, RTT to Tier 2, privacy tier, clinical urgency).
• 3. Agents compute a bid using the provided weighted formula.
• 4. The broker awards the task to the lowest bidder.
• 5. All agents observe the outcome of tasks they did not win, providing counterfactual feedback.
• 6. Agents update their bid policy (actor‑critic loop) based on observed outcomes.
• 7. The process repeats for subsequent tasks, dynamically balancing load across tiers.

证据

When I gave each agent a small "embodied" feedback signal — a local observation of its own compute, thermal, and memory state — the coordination policy converged nearly three times faster than a purely centralized scheduler.

来源

A single whole-slide image (WSI) from a modern digital pathology scanner can exceed 100,000 × 100,000 pixels — several gigabytes of data.

来源

HIPAA, GDPR, and institutional IRBs often prohibit raw patient data from leaving the premises.

来源