Accelerating Collaborative Edge Learning through Verifiable Data-Centric Gossip Protocol

摘要

With the rapid progress in artificial intelligence (AI), the network edge has become a dominant source of context-rich data, which demands immediate processing and seamless sharing to optimize AI-as-a-service models. Edge AI (or edge intelligence) enables on-device inference and localized training without central coordination. This paradigm requires the timely exchange of model updates among distributed devices to ensure efficient collaboration and consistent model convergence under constrained resources. However, existing approaches are limited by bandwidth asymmetry, heterogeneous device churn, and model integrity under adversarial updates. Herein, we propose DcGossip, a data-centric gossip-based model update sharing approach that leverages information-centric networking to enable receiver-driven model retrieval with named, content-addressable updates, adaptive model discovery, and version-controlled exchange for communication-efficient collaborative edge learning. DcGossip also introduces a self-verifying model integrity verification mechanism to ensure secure propagation and tamper-proof validation of shared updates. Our extensive evaluations demonstrate that DcGossip achieves 41% faster convergence speed and 26% lower propagation latency, effectively reducing communication cost by 80% compared to existing approaches, while maintaining minimal verification overhead.