  Federated learning (FL), an emerging distributed machine learning paradigm,
in conflux with edge computing is a promising area with novel applications over
mobile edge devices. In FL, since mobile devices collaborate to train a model
based on their own data under the coordination of a central server by sharing
just the model updates, training data is maintained private. However, without
the central availability of data, computing nodes need to communicate the model
updates often to attain convergence. Hence, the local computation time to
create local model updates along with the time taken for transmitting them to
and from the server result in a delay in the overall time. Furthermore,
unreliable network connections may obstruct an efficient communication of these
updates. To address these, in this paper, we propose a delay-efficient FL
mechanism that reduces the overall time (consisting of both the computation and
communication latencies) and communication rounds required for the model to
converge. Exploring the impact of various parameters contributing to delay, we
seek to balance the trade-off between wireless communication (to talk) and
local computation (to work). We formulate a relation with overall time as an
optimization problem and demonstrate the efficacy of our approach through
extensive simulations.
