Blockchain-based crowdsourced deep reinforcement learning as a service

2024-06-01

Information Sciences

AI Summary

To address the high adoption barrier of deep reinforcement learning (DRL) and the lack of trustworthy mechanisms for collaborative training, this paper proposes the first blockchain-enabled decentralized DRL crowdsourced training framework. The framework integrates a permissioned blockchain (Hyperledger Fabric), federated reinforcement learning, differential privacy, and smart contracts to ensure verifiable training, traceable accountability, and fair reward distribution. Evaluated on CartPole and traffic signal control tasks, it achieves a 23% faster policy convergence compared to conventional federated learning and improves model robustness by 37% under adversarial participant attacks. Its core contributions are: (i) introducing the first DRL crowdsourced training paradigm; (ii) resolving trust bottlenecks and the privacy-utility trade-off in multi-party collaboration; and (iii) providing a novel pathway toward scalable, secure, and production-ready DRL deployment.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Problem

• Deep Reinforcement Learning
• Simplicity
• Complex Problem Solving

Innovation

• DRLaaS
• Blockchain
• IPFS