Decentralized Plasticity in Reservoir Dynamical Networks for Pervasive Environments

Abstract

We propose a framework for localized learning with Reservoir Computing dynamical neural systems in pervasive environments, where data is distributed and dynamic. We use biologically plausible intrinsic plasticity (IP) learning to optimize the non-linearity of system dynamics based on local objectives, and extend it to account for data uncertainty. We develop two algorithms for federated and continual learning, FedIP and FedCLIP, which respectively extend IP to client-server topologies and to prevent catastrophic forgetting in streaming data scenarios. Results on real-world datasets from human monitoring show that our approach improves performance and robustness, while preserving privacy and efficiency.