Multiple Kernel-Based Online Federated Learning
This work addresses communication efficiency for edge nodes in federated learning, though it is an incremental improvement over existing methods.
The paper tackles the heavy communication overhead in extending online multiple kernel learning to online federated learning by proposing a novel method that achieves the same performance with a 1/P reduction in overhead, and it demonstrates optimal sublinear regret bounds and practicality on real-world datasets.
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.