  In instances of online kernel learning where little prior information is
available and centralized learning is unfeasible, past research has shown that
distributed and online multi-kernel learning provides sub-linear regret as long
as every pair of nodes in the network can communicate (i.e., the communications
network is a complete graph). In addition, to manage the communication load,
which is often a performance bottleneck, communications between nodes can be
quantized. This letter expands on these results to non-fully connected graphs,
which is often the case in wireless sensor networks. To address this challenge,
we propose a gossip algorithm and provide a proof that it achieves sub-linear
regret. Experiments with real datasets confirm our findings.
