Device Sampling and Resource Optimization for Federated Learning in
  Cooperative Edge Networks
Su Wang Roberto Morabito Seyyedali Hosseinalipour Mung Chiang Christopher G. Brinton
The conventional federated learning (FedL) architecture distributes machine
learning (ML) across worker devices by having them train local models that are
periodically aggregated by a server. FedL ignores two important characteristics
of contemporary wireless networks, however: (i) the network may contain
heterogeneous communication/computation resources, and (ii) there may be
significant overlaps in devices' local data distributions. In this work, we
develop a novel optimization methodology that jointly accounts for these
factors via intelligent device sampling complemented by device-to-device (D2D)
offloading. Our optimization methodology aims to select the best combination of
sampled nodes and data offloading configuration to maximize FedL training
accuracy while minimizing data processing and D2D communication resource
consumption subject to realistic constraints on the network topology and device
capabilities. Theoretical analysis of the D2D offloading subproblem leads to
new FedL convergence bounds and an efficient sequential convex optimizer. Using
these results, we develop a sampling methodology based on graph convolutional
networks (GCNs) which learns the relationship between network attributes,
sampled nodes, and D2D data offloading to maximize FedL accuracy. Through
evaluation on popular datasets and real-world network measurements from our
edge testbed, we find that our methodology outperforms popular device sampling
methodologies from literature in terms of ML model performance, data processing
overhead, and energy consumption.