In 5G and beyond networks, the increasing inclusion of heterogeneous smart devices and the rising privacy and security concerns, are two crucial challenges in terms of computation complexity and privacy preservation for Artificial Intelligence (AI)-based solutions. In this regard, federated learning emerges as a new technique, which enlarges the scale of training data, and protects the privacy of user data. The development of edge computing makes it possible to apply federated learning to beyond 5G. However, the security of local parameters, the learning quality, and the varying computing and communication resources, are crucial issues that remain unexplored in federated learning schemes. In this article, we propose a block-chain empowered federated learning framework, and present its potential application scenarios in beyond 5G. We enhance the security and privacy by integrating blockchain into a federated learning scheme for maintaining the trained parameters. In particular, we formulate the resource sharing task as a combinational optimization problem while taking resource consumption and learning quality into account. We design a deep reinforcement learning based algorithm to find an optimal solution to the problem. Numerical results show that the proposed scheme achieves high accuracy and good convergence.

中文翻译：

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超越5G的区块链和联合学习

在5G及以后的网络中，基于人工智能（AI）的解决方案在计算复杂性和隐私保护方面，异构智能设备的日益普及以及日益增长的隐私和安全问题是两个关键挑战。在这方面，联合学习作为一种新技术出现，它扩大了训练数据的规模，并保护了用户数据的隐私。边缘计算的发展使得将联合学习应用于5G以外的领域成为可能。但是，局部参数的安全性，学习质量以及变化的计算和通信资源是至关重要的问题，在联合学习方案中仍未探索。在本文中，我们提出了一个区块链授权的联合学习框架，并提出了其在5G以外的潜在应用场景。我们通过将区块链集成到联邦学习方案中以维护训练有素的参数来增强安全性和隐私性。特别是，我们在考虑资源消耗和学习质量的同时，将资源共享任务公式化为组合优化问题。我们设计了一种基于深度强化学习的算法，以找到该问题的最佳解决方案。数值结果表明，该方案具有较高的精度和收敛性。我们设计了一种基于深度强化学习的算法，以找到该问题的最佳解决方案。数值结果表明，该方案具有较高的精度和收敛性。我们设计了一种基于深度强化学习的算法，以找到该问题的最佳解决方案。数值结果表明，该方案具有较高的精度和收敛性。