Decentralized Online Learning with Kernels

Understand

We consider multi-agent stochastic optimization problems over reproducing kernel Hilbert spaces (RKHS).

• In this setting, a network of interconnected agents aims to learn decision functions, i.e., nonlinear statistical models, that are optimal in terms of a global convex functional that aggregates data across the network, with only access to locally and sequentially observed samples.
• We propose solving this problem by allowing each agent to learn a local regression function while enforcing consensus constraints.
• We use a penalized variant of functional stochastic gradient descent operating simultaneously with low-dimensional subspace projections.