用区块链让机器人集群无中心化同步模型，防故障干扰。
Securing Federated Learning in Robot Swarms using Blockchain Technology
- 用区块链实现去中心化模型聚合，无需中央服务器
- 单个故障机器人可严重破坏训练，实验验证了风险
- 通过智能合约构建防篡改保护机制，适合分布式系统研究者
联邦学习是一种分布式机器学习新方法，可降低通信开销并分摊训练成本，适用于群体机器人应用。然而，传统联邦学习通常依赖中心化服务器进行模型聚合。本文提出一种无需中心服务器的群体机器人联邦学习原型系统，利用区块链技术实现机器人集群安全同步共享模型。实验在基于物理仿真的ARGoS平台中进行，采用以太坊区块链协议，由每个仿真机器人独立执行。结果表明，单一故障机器人可能严重干扰训练过程。为此，我们设计了基于安全、不可篡改区块链智能合约的防护机制，有效提升系统鲁棒性。
原文摘要 · Abstract (English)
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.
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