The combination of artificial intelligence and mobile edge computing (MEC) is considered as a promising evolution path of the future wireless networks. As a model-level coordination learning paradigm, federated learning can make full use of the distributed computation resource in the MEC systems, which allows the users to keep their private data locally. However, due to the unreliable wireless transmission circumstances and resource constraints in the MEC systems, both the performance and training efficiency of federated learning cannot be guaranteed. To solve this problem, the optimization design of federated learning in the MEC systems is studied in this paper. First, an optimization problem is formulated to manage the tradeoff between model accuracy and training cost. Second, a joint optimization algorithm is designed to optimize the model compression, sample selection, and user selection strategies, which can approach a stationary optimal solution in a computationally efficient way. Finally, the performance of our proposed optimization scheme is evaluated by numerical simulation and experiment results, which show that both the accuracy loss and the cost of federated learning in the MEC systems can be reduced significantly by employing our proposed algorithm.

中文翻译：

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移动边缘计算系统中联邦学习的设计


人工智能与移动边缘计算（MEC）的结合被认为是未来无线网络有前途的演进路径。作为一种模型级协调学习范式，联邦学习可以充分利用MEC系统中的分布式计算资源，允许用户将私有数据保存在本地。然而，由于MEC系统中不可靠的无线传输环境和资源限制，联邦学习的性能和训练效率都无法得到保证。针对这一问题，本文研究了MEC系统中联邦学习的优化设计。首先，制定优化问题来管理模型准确性和训练成本之间的权衡。其次，设计了联合优化算法来优化模型压缩、样本选择和用户选择策略，该算法可以以计算有效的方式逼近平稳最优解。最后，通过数值模拟和实验结果评估了我们提出的优化方案的性能，结果表明，采用我们提出的算法可以显着降低MEC系统中联邦学习的精度损失和成本。