Communication-Efficient Decentralized Dynamic Kernel Learning

This paper studies the decentralized dynamic kernel learning problem where each agent in the network receives continuous streaming local data and works collaboratively to learn a non-linear function "on the fly" in a dynamic environment. We utilize the random feature (RF) mapping method to circumvent the curse of dimensionality issue in conventional kernel methods and reformulate the dynamic kernel learning problem as a dynamic parameter optimization problem, which is then efficiently solved by the Decentralized Dynamic Kernel Learning via ADMM (DDKL) framework. To further improve communication efficiency, we incorporate the quantization and censoring strategies in the communication stage and develop the Quantized and Communication-censored DDKL (QC-DDKL) algorithm. We theoretically prove that QC-DDKL can achieve the optimal sublinear regret $\mathcal{O}(\sqrt T )$ over T time slots. Simulation results also corroborate the learning effectiveness and the communication efficiency of the proposed method.