  The exponential growth of devices and data at the edges of the Internet is
rising scalability and privacy concerns on approaches based exclusively on
remote cloud platforms. Data gravity, a fundamental concept in Fog Computing,
points towards decentralisation of computation for data analysis, as a viable
alternative to address those concerns. Decentralising AI tasks on several
cooperative devices means identifying the optimal set of locations or
Collection Points (CP for short) to use, in the continuum between full
centralisation (i.e., all data on a single device) and full decentralisation
(i.e., data on source locations). We propose an analytical framework able to
find the optimal operating point in this continuum, linking the accuracy of the
learning task with the corresponding network and computational cost for moving
data and running the distributed training at the CPs. We show through
simulations that the model accurately predicts the optimal trade-off, quite
often an intermediate point between full centralisation and full
decentralisation, showing also a significant cost saving w.r.t. both of them.
Finally, the analytical model admits closed-form or numeric solutions, making
it not only a performance evaluation instrument but also a design tool to
configure a given distributed learning task optimally before its deployment.
