SwarmUpdate: Hierarchical Software Updates and Deep Learning Model Patching for Heterogeneous UAV Swarms

2025-03-18

AI Summary

To address the low software and deep learning model co-updating efficiency, high communication overhead, and poor adaptability in heterogeneous UAV swarms under dynamic tasks, this paper proposes SwarmSync—a hierarchical synchronization protocol—and SwarmModelPatch—a selective layer-freezing incremental model patching method. SwarmSync employs a topology-aware hierarchical gossip mechanism to reduce update latency and bandwidth consumption. SwarmModelPatch enables layer-granular selective freezing and fine-tuning of models, coupled with an auction-based mechanism for optimized patch distribution. Evaluated on the large-scale ARGoS simulation platform, experimental results demonstrate that SwarmSync significantly improves synchronization efficiency. SwarmModelPatch compresses model update size by 67% while incurring less than 1.2% accuracy degradation, effectively balancing heterogeneity, real-time responsiveness, and lightweight deployment requirements.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Heterogeneous unmanned aerial vehicle (UAV) swarms consist of dozens to hundreds of drones with different roles and varying hardware and software requirements collaborating towards a shared mission. While traditional approaches for synchronized software updates assume swarms to be unstructured and homogeneous, the heterogeneous nature of modern swarms and the emerging need of drones to update their deep learning (perception) models with new objectives or data as a mission unfolds, has made efficient software update methods crucial for swarms to adapt to dynamic environments. To address these challenges, we introduce the SwarmUpdate framework for software updates in heterogeneous UAV swarms, composed of two key components: SwarmSync and SwarmModelPatch. SwarmSync is a hierarchical software update synchronization strategy to distribute a software update to the right subset of drones within a swarm, while SwarmModelPatch is a deep learning model patching method that reduces the size of a (deep learning model) update by only allowing some layers of the model to be updated (freezing the other layers). In this paper, we systematically evaluate the performance of SwarmSync through large-scale simulations in the ARGoS swarm simulator, comparing SwarmSync to auction-based (SOUL) and gossip-based rebroadcasting (Gossip) baselines, and SwarmModelPatch to a non-incremental model patching strategy.

Problem

• Efficient software updates for heterogeneous UAV swarms
• Deep learning model patching during mission execution
• Hierarchical synchronization for targeted drone updates

Innovation

• Hierarchical software update synchronization strategy
• Deep learning model patching method
• Large-scale simulations in ARGoS swarm simulator