… a fast-growing area called Federated Learning (FL), a distributed deep learning paradigm
[8]. It allows vehicles to use local data to train their local deep learning models individually and …

… privacy leakage and limited communication resources, federated learning (FL) has … allocation,
block size adjustment, and block producer selection jointly. Since the remaining resources…

… the resource allocation among MUs to reduce the communication latency in learning iterations.
… We show that this hierarchical federated learning (HFL) scheme significantly reduces the …

… offloading delay and network resource usage by jointly optimizing computation offloading,
resource allocation, and service caching placement. We also leverage federated learning (FL) …

… Abstract—In this paper, the problem of training federated learning (FL) algorithms over a
realistic wireless network is studied. In particular, in the considered model, wireless users …

… analysis and model training at the network edge, as well as the rising concerns about the
data privacy, a new distributed training framework called federated learning (FL) has emerged. …

… only to be integrated with machine learning applications, but also to preserve privacy and
be energy-efficient. Federated learning (FL) is a distributed learning framework that offers high …

… One approach to mitigate these problems is federated learning (FL), which enables the
devices to train a common machine learning model without data sharing and transmission. This …

… learning directly due to the unique challenges of information … for federated learning to
motivate edge nodes to contribute model training. Specifically, a deep reinforcement learning-…

… Abstract—This paper studies a federated learning (FL) system, where multiple FL services
… share common wireless resources. It fills the void of wireless resource allocation for multiple …