How can I build a private, efficient multi-drone AI?
On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarms
March 25, 2025
https://arxiv.org/pdf/2503.17436This research demonstrates on-device federated continual learning (ODFCL) for face recognition on a swarm of resource-constrained nano-drones. It uses a regularized version of federated learning (FedProx) combined with a continual learning technique (MOL) on a RISC-V based multi-core SoC to allow the drones to learn new faces incrementally without forgetting previously learned ones, while preserving data privacy by avoiding raw data sharing.
Key points for LLM-based multi-agent systems:
- Decentralized Learning: The federated learning approach allows multiple agents (drones) to collaboratively train a shared model without exchanging raw data, a valuable concept for privacy-preserving multi-agent LLM applications.
- Continual Learning: The ability to incrementally learn new information (new faces) without forgetting prior knowledge is crucial for long-term deployment of LLM-based agents in dynamic environments.
- Resource Efficiency: Demonstrating this technique on a low-power embedded system is relevant for deploying complex LLM agents on resource-constrained devices, potentially expanding the reach of multi-agent LLM applications beyond server environments.
- Specialization & Collaboration: Each drone can potentially specialize in recognizing specific sets of faces and then contribute to a shared, more comprehensive model through the federated learning process. This mirrors specialized roles within a collaborative multi-agent LLM system.