Trustless intelligent rooms: a blockchain-enabled federated learning framework with lightweight neural networks for privacy-preserving healthcare

Abstract

The rapid aging of the global population necessitates automated healthcare environments, yet current Intelligent Room architectures relying on centralized cloud servers face critical challenges regarding data opacity and single points of failure. This paper proposes a novel architecture that synergizes Distributed Ledger Technology (DLT) with Federated Learning (FL) to create a trustless, immutable audit trail for patient monitoring. Unlike traditional FL approaches, we introduce a blockchain-based aggregation mechanism that eliminates the central authority. Furthermore, to address the resource constraints of edge devices such as smartphones, we implement a specific Lightweight Neural Network (L-CNN) utilizing depthwise separable convolutions. The proposed system ensures that patient data remains local while model updates are cryptographically verified on-chain, offering a scalable, low-cost solution for resource-constrained healthcare environments.