  In this paper, the problem of minimizing energy and time consumption for task
computation and transmission is studied in a mobile edge computing
(MEC)-enabled balloon network. In the considered network, each user needs to
process a computational task in each time instant, where high-altitude balloons
(HABs), acting as flying wireless base stations, can use their powerful
computational abilities to process the tasks offloaded from their associated
users. Since the data size of each user's computational task varies over time,
the HABs must dynamically adjust the user association, service sequence, and
task partition scheme to meet the users' needs. This problem is posed as an
optimization problem whose goal is to minimize the energy and time consumption
for task computing and transmission by adjusting the user association, service
sequence, and task allocation scheme. To solve this problem, a support vector
machine (SVM)-based federated learning (FL) algorithm is proposed to determine
the user association proactively. The proposed SVM-based FL method enables each
HAB to cooperatively build an SVM model that can determine all user
associations without any transmissions of either user historical associations
or computational tasks to other HABs. Given the prediction of the optimal user
association, the service sequence and task allocation of each user can be
optimized so as to minimize the weighted sum of the energy and time
consumption. Simulations with real data of city cellular traffic from the
OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can
reduce the weighted sum of the energy and time consumption of all users by up
to 16.1% compared to a conventional centralized method.
