Incentive Sharing: A Consumer-Data Privacy Trusted Sharing Paradigm via Proximal Policy Optimization in Industry 5.0
Ting Cai, Ying Hu, Xiang Li, Wuhui Chen, Yuxin Wu, Zhiwei Ỹe, Zibin Zheng · IEEE Transactions on Consumer Electronics · 2024
Industry 5.0 emphasizes consumer data sharing to drive data-driven innovation and enhance user experiences in consumer electronics. Blockchain-integrated federated learning (blockchained FL) has recently emerged as a privacy-trusted paradigm to facilitate sharing. However, most blockchained FL sharing paradigms cannot adapt to the complexity or variability of consumer data and network conditions such as: 1) inadequate balancing of the trade-off between data quality and training costs during device selection; 2) incentives are designed to maximize instantaneous profit without considering the long-term dynamics of electronic devices. These limitations lead to low-quality model training and security degradation. This paper proposes a novel blockchained FL incentive-sharing paradigm that dynamically selects consumer devices from a long-term perspective while balancing data quality and training costs. Specifically, we first present a reputation strategy to enhance security by applying a predefined threshold to prevent malicious and low-quality consumers from participating in FL. Then, to optimize device selection under complex and dynamic consumer environments with high-dimensional system states, a Proximal Policy Optimization (PPO)-based incentive mechanism is proposed, which includes: 1) building an “intelligent engine” to select the optimal consumer devices for each round of FL based on data quality and costs, and 2) implementing incentives to achieve long-term maximization of the system’s social welfare. Extensive simulations show that our sharing paradigm has, on average, 46.97% higher social welfare and 77.27% lower social costs than baselines and can guarantee high data quality in a trustless environment.