Gossiped and Quantized Online Multi-Kernel Learning
This work addresses communication bottlenecks in distributed learning for scenarios like wireless sensor networks, but it is incremental as it extends prior results from complete graphs to more general networks.
The paper tackles the problem of online multi-kernel learning in distributed settings with non-fully connected graphs, such as wireless sensor networks, by proposing a gossip algorithm that achieves sub-linear regret, as confirmed by experiments on real datasets.
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.