ブロックチェーン技術によるロボット群の連合学習の保護
Securing Federated Learning in Robot Swarms using Blockchain Technology
中央サーバーなしでロボット群が連合学習を行うため、ブロックチェーンでモデルを安全に同期する仕組みを提案し、故障ロボット対策のスマートコントラクトを実装・検証した。
著者: Alexandre Pacheco, Sébastien De Vos, Andreagiovanni Reina, Marco Dorigo, Volker Strobel
分類: cs.RO
原文アブストラクト
Federated learning is a new approach to distributed machine learning that offers potential advantages such as reducing communication requirements and distributing the costs of training algorithms. Therefore, it could hold great promise in swarm robotics applications. However, federated learning usually requires a centralized server for the aggregation of the models. In this paper, we present a proof-of-concept implementation of federated learning in a robot swarm that does not compromise decentralization. To do so, we use blockchain technology to enable our robot swarm to securely synchronize a shared model that is the aggregation of the individual models without relying on a central server. We then show that introducing a single malfunctioning robot can, however, heavily disrupt the training process. To prevent such situations, we devise protection mechanisms that are implemented through secure and tamper-proof blockchain smart contracts. Our experiments are conducted in ARGoS, a physics-based simulator for swarm robotics, using the Ethereum blockchain protocol which is executed by each simulated robot.
