  This work poses a distributed multi-resource allocation scheme for minimizing
the weighted sum of latency and energy consumption in the on-device distributed
federated learning (FL) system. Each mobile device in the system engages the
model training process within the specified area and allocates its computation
and communication resources for deriving and uploading parameters,
respectively, to minimize the objective of system subject to the
computation/communication budget and a target latency requirement. In
particular, mobile devices are connect via wireless TCP/IP architectures.
Exploiting the optimization problem structure, the problem can be decomposed to
two convex sub-problems. Drawing on the Lagrangian dual and harmony search
techniques, we characterize the global optimal solution by the closed-form
solutions to all sub-problems, which give qualitative insights to
multi-resource tradeoff. Numerical simulations are used to validate the
analysis and assess the performance of the proposed algorithm.
