  Online federated learning (OFL) becomes an emerging learning framework, in
which edge nodes perform online learning with continuous streaming local data
and a server constructs a global model from the aggregated local models. Online
multiple kernel learning (OMKL), using a preselected set of P kernels, can be a
good candidate for OFL framework as it has provided an outstanding performance
with a low-complexity and scalability. Yet, an naive extension of OMKL into OFL
framework suffers from a heavy communication overhead that grows linearly with
P. In this paper, we propose a novel multiple kernel-based OFL (MK-OFL) as a
non-trivial extension of OMKL, which yields the same performance of the naive
extension with 1/P communication overhead reduction. We theoretically prove
that MK-OFL achieves the optimal sublinear regret bound when compared with the
best function in hindsight. Finally, we provide the numerical tests of our
approach on real-world datasets, which suggests its practicality.
