There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data. Despite its advantages in data privacy-preserving, Federated Learning (FL) still has challenges in heterogeneity across users' data and UE's characteristics. We first address the heterogeneous data challenge by proposing a FL algorithm that can bypass the independent and identically distributed (i.i.d.) UEs' data assumption for strongly convex and smooth problems. We provide the convergence rate characterizing the trade-off between local computation rounds of UE to update its local model and global communication rounds to update the global model. We then employ the proposed FL algorithm in wireless networks as a resource allocation optimization problem that captures various trade-offs between computation and communication latencies as well as between the Federated Learning time and UE energy consumption. Even though the wireless resource allocation problem of FL is non-convex, we exploit this problem's structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights to problem design. Finally, we illustrate the theoretical analysis for the new algorithm with Tensorflow experiments and extensive numerical results for the wireless resource allocation sub-problems. The experiment results not only verify the theoretical convergence but also show that our proposed algorithm converges significantly faster than the existing baseline approach.

提出了一种处理异构用户设备数据的联邦学习算法，同时结合对微波资源分配问题的优化，实现了收敛速率和能耗之间的平衡，并用Tensorflow实验验证了该算法的理论分析和优化表现。

无线网络上的联邦学习：收敛分析和资源分配