AeroKV: A Resilient Collaborative Large Model Inference System for UAV Swarms

Title: AeroKV: A Resilient Collaborative Large Model Inference System for UAV Swarms

Abstract: Open-source implementation of a MILCOM 2026 paper, proposing a lifespan-aware resilient collaborative LLM inference framework for UAV swarms to address distributed large model inference challenges in resource-constrained environments.

Open-source Info: Original author/maintainer hzhou10cs, source GitHub, project link https://github.com/hzhou10cs/Resilient-Collaborative-LLM-Inference-for-UAV-Swarms, release date 2026-05-28.

Core: Addressing resource constraints of UAV swarms, enabling large model inference via collaborative reasoning, lifespan-aware scheduling, and resilient fault-tolerance mechanisms.

Research Background

With the enhanced capabilities of Large Language Models (LLMs), the demand for deploying them on edge/UAV platforms is growing. However, individual UAVs have limited computing power, memory, and battery, making it difficult to independently support LLM inference.

Core Challenges

1. Computing Resource Constraints: Consumer-grade UAVs have far less computing power/memory than data center GPUs, so single nodes can't efficiently complete inference.
2. Energy Constraints: High energy consumption shortens flight time; need to balance inference quality and battery life.
3. Dynamic Topology and Failures: Cluster nodes may join/leave at any time; the system needs to adapt to dynamics.

AeroKV System Architecture

Core Concept: Lifespan-aware Resilient Collaborative Inference

Collaborative Inference Model

Adopts model sharding + pipeline parallelism technology, distributing different layers of the large model to different UAVs so the cluster collaboratively completes full inference.

Lifespan-aware Scheduling

Monitors remaining battery, load, and network status of nodes in real time, dynamically adjusting task allocation: low-battery nodes are assigned light tasks to extend their participation time.

Resilient Fault-tolerance Mechanism

Automatically reallocates tasks when nodes fail/leave to ensure uninterrupted inference service.

Key Technical Implementation Points

Communication Optimization

Addressing the limited bandwidth issue of UAV wireless ad-hoc networks (MANET), uses compression technology and intelligent retransmission strategies to reduce communication overhead.

Memory Management

Solves the problem of edge devices' limited memory preventing large model operation via weight sharing and dynamic loading mechanisms.

Energy Consumption Model

Establishes an inference energy consumption prediction model to provide a basis for scheduling decisions.

Application Scenarios

1. Search and Rescue Tasks: Real-time analysis of on-site data to identify trapped people/hazardous areas.
2. Agricultural Monitoring: Collaboratively analyze crop images to identify pests/diseases and generate reports.
3. Border Patrol: Analyze video streams to detect anomalies and generate descriptive reports.
4. Military Applications: Battlefield situation awareness, target recognition, intelligence analysis (adapted to MILCOM scenarios).

Technical Insights

1. Resource-constrained environments can run large models via collaboration + intelligent scheduling, promoting AI popularization on edge devices.
2. The resilient design concept has reference value for stable service of distributed AI systems.
3. The trade-off between energy consumption and performance is of great significance for mobile AI applications.

Summary

AeroKV is an innovative attempt to push large models to extreme edge environments, providing reference implementations and ideas for edge AI, distributed systems, and resource optimization fields.

Limitations

Currently still faces issues such as network latency, secure communication, and the impact of harsh weather.

Future Outlook

In the future, combining model compression technologies (quantization, pruning, knowledge distillation) and dedicated edge AI chips can further enhance the inference capabilities of UAV swarms.