On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarms

• URL: http://arxiv.org/abs/2503.17436v2
• Date: Mon, 21 Apr 2025 23:02:42 GMT
• Title: On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarms
• Authors: Lars Kröger, Cristian Cioflan, Victor Kartsch, Luca Benini,
• Abstract summary: We propose a regularization-based On-Device Federated Continual Learning algorithm tailored for multiple nano-drones performing face recognition tasks.<n>We improve the classification accuracy by 24% over naive fine-tuning, requiring 178 ms per local epoch and 10.5 s per global epoch.
• Score: 12.296600495357843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RISC-V-based architectures are paving the way for efficient On-Device Learning (ODL) in smart edge devices. When applied across multiple nodes, ODL enables the creation of intelligent sensor networks that preserve data privacy. However, developing ODL-capable, battery-operated embedded platforms presents significant challenges due to constrained computational resources and limited device lifetime, besides intrinsic learning issues such as catastrophic forgetting. We face these challenges by proposing a regularization-based On-Device Federated Continual Learning algorithm tailored for multiple nano-drones performing face recognition tasks. We demonstrate our approach on a RISC-V-based 10-core ultra-low-power SoC, optimizing the ODL computational requirements. We improve the classification accuracy by 24% over naive fine-tuning, requiring 178 ms per local epoch and 10.5 s per global epoch, demonstrating the effectiveness of the architecture for this task.

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