  This paper develops a novel mathematical framework for collaborative learning
by means of geometrically inspired kernel machines which includes statements on
the bounds of generalisation and approximation errors, and sample complexity.
For classification problems, this approach allows us to learn bounded geometric
structures around given data points and hence solve the global model learning
problem in an efficient way by exploiting convexity properties of the related
optimisation problem in a Reproducing Kernel Hilbert Space (RKHS). In this way,
we can reduce classification problems to determining the closest bounded
geometric structure from a given data point. Further advantages that come with
our solution is that our approach does not require clients to perform multiple
epochs of local optimisation using stochastic gradient descent, nor require
rounds of communication between client/server for optimising the global model.
We highlight that numerous experiments have shown that the proposed method is a
competitive alternative to the state-of-the-art.
