  Federated Learning (FL) is an intriguing distributed machine learning
approach due to its privacy-preserving characteristics. To balance the
trade-off between energy and execution latency, and thus accommodate different
demands and application scenarios, we formulate an optimization problem to
minimize a weighted sum of total energy consumption and completion time through
two weight parameters. The optimization variables include bandwidth,
transmission power and CPU frequency of each device in the FL system, where all
devices are linked to a base station and train a global model collaboratively.
Through decomposing the non-convex optimization problem into two subproblems,
we devise a resource allocation algorithm to determine the bandwidth
allocation, transmission power, and CPU frequency for each participating
device. We further present the convergence analysis and computational
complexity of the proposed algorithm. Numerical results show that our proposed
algorithm not only has better performance at different weight parameters (i.e.,
different demands) but also outperforms the state of the art.
