… Furthermore, we evaluate the impact of the resource allocation scheme on the learning
performance in Fig. 3. For performance comparison, we also implement the equal …

… We then consider incorporating federated learning (FL) into the design of MAR systems for
the Metaverse. FL can collaboratively train a machine learning model while preserving data …

To exploit the massive amounts of onboard data in vehicular networks while protecting data
privacy and security, federated learning (FL) is regarded as a promising technology to …

… research directions of federated learning. Finally, we summarize the characteristics of
existing federated learning, and analyze the current practical application of federated learning. …

… computation and communication resource allocation in such a … and communication resource
allocation problems as the … and communication resource allocation subproblems using …

… machine learning techniques that require no communication resources, federated learning
… federated learning model becomes critical to performance optimization. On the other hand, …

… Federated Learning (FL) is proposed to solve the … allocation, computing Central Processing
Unit (CPU) frequency, optimization transmission power, and the desired level of learning …

… We propose a new content-based vehicle client selection and resource allocation algorithm
for federated learning in the Internet of Vehicles. In this paper, we analyze it by training …

… the batchsize selection and communication resource allocation for the acceleration of the FL
… In [15], the authors developed a federated learning based spiking neural network. However, …

… problem of the training client scheduling and resource allocation (ie, the transmit and … the
learning performance of FL. To this end, we propose a dynamic resource allocation and client …