Abstract
In this paper, federated learning (FL) over wireless networks is
investigated. In each communication round, a subset of devices is selected to
participate in the aggregation with limited time and energy. In order to
minimize the convergence time, global loss and latency are jointly considered
in a Stackelberg game based framework. Specifically, age of information (AoI)
based device selection is considered at leader-level as a global loss
minimization problem, while sub-channel assignment, computational resource
allocation, and power allocation are considered at follower-level as a latency
minimization problem. By dividing the follower-level problem into two
sub-problems, the best response of the follower is obtained by a monotonic
optimization based resource allocation algorithm and a matching based
sub-channel assignment algorithm. By deriving the upper bound of convergence
rate, the leader-level problem is reformulated, and then a list based device
selection algorithm is proposed to achieve Stackelberg equilibrium. Simulation
results indicate that the proposed device selection scheme outperforms other
schemes in terms of the global loss, and the developed algorithms can
significantly decrease the time consumption of computation and communication.