# SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution

arXiv: https://arxiv.org/abs/2603.20233 · HTML: https://mlsanity.com/paper/2603.20233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Cross-Model LLM Code Review: Should you use Claude to review Codex or vice versa?](https://mlsanity.com/paper/2607.21656/) (2026)

## Abstract (arXiv)

Federated robotic task execution systems require bridging natural language instructions to distributed robot control while efficiently managing computational resources across heterogeneous edge devices without centralized coordination. Existing approaches face three limitations: rigid hand-coded planners requiring extensive domain engineering, centralized coordination that contradicts federated collaboration as robots scale, and static resource allocation failing to share containers across robots when workloads shift dynamically. We present SwiftBot, a federated task execution platform that integrates LLM-based task decomposition with intelligent container orchestration over a DHT overlay, enabling robots to collaboratively execute tasks without centralized control. SwiftBot achieves 94.3% decomposition accuracy across diverse tasks, reduces task startup latency by 1.5-5.4x and average training latency by 1.4-2.5x, and improves tail latency by 1.2-4.7x under high load through federated warm container migration. Evaluation on multimedia tasks validates that co-designing semantic understanding and federated resource management enables both flexibility and efficiency for robotic task control.