  We investigate a cooperative federated learning framework among devices for
mobile edge computing, named CFLMEC, where devices co-exist in a shared
spectrum with interference. Keeping in view the time-average network throughput
of cooperative federated learning framework and spectrum scarcity, we focus on
maximize the admission data to the edge server or the near devices, which fills
the gap of communication resource allocation for devices with federated
learning. In CFLMEC, devices can transmit local models to the corresponding
devices or the edge server in a relay race manner, and we use a decomposition
approach to solve the resource optimization problem by considering maximum data
rate on sub-channel, channel reuse and wireless resource allocation in which
establishes a primal-dual learning framework and batch gradient decent to learn
the dynamic network with outdated information and predict the sub-channel
condition. With aim at maximizing throughput of devices, we propose
communication resource allocation algorithms with and without sufficient
sub-channels for strong reliance on edge servers (SRs) in cellular link, and
interference aware communication resource allocation algorithm for less
reliance on edge servers (LRs) in D2D link. Extensive simulation results
demonstrate the CFLMEC can achieve the highest throughput of local devices
comparing with existing works, meanwhile limiting the number of the
sub-channels.
