SONAR Web: A Platform-Agnostic Framework for Real-Time Decentralized Learning Across Heterogeneous Edge Clients

Abstract

Most federated learning (FL) frameworks assume reliable networks and homogeneous devices, limiting their applicability in mobile and edge environments where connectivity is intermittent and devices are highly heterogeneous. We introduce SONAR Web, an open-source framework for fully decentralized, cross-platform collaborative learning between browsers, servers, tablets, and smartphones. SONAR Web decouples the learning protocol from the underlying client platform through a platform-agnostic configuration interface—enabling Python, JavaScript, and mobile clients to seamlessly interoperate in real time. By combining peer-to-peer RTC protocols with communication-efficient techniques from FL, SONAR Web supports privacy-preserving training without centralized orchestration. We demonstrate SONAR Web's robustness through deployments on real-world devices and networks, showing resilience under heterogeneous network conditions and resource variability. SONAR Web provides a unified, language-agnostic interface for decentralized learning, enabling seamless collaboration across heterogeneous devices and runtimes—advancing scalable, inclusive, and real-time model training at the mobile and edge frontier.