Robust Distributed Cooperative Classification With Learned Compressed-Feature Diffusion.

Abstract

Cooperative inference in distributed sensor networks is challenged by limited communication bandwidth and the risk of node failures. This paper introduces Compressed Feature Diffusion for Decentralized Classification (CFD-DC), a novel framework that addresses these challenges. Each node performs local inference using its own features and compressed feature representations received from other nodes. Our approach relies on two key components: first, a trainable feature compressor at each node that learns compact representations, reducing communication while preserving critical discriminative information; second, an adaptive node weighting mechanism that dynamically adjusts the influence of local and remote features, providing robustness to unreliable or failed nodes. Experiments on multi-view image classification and a simulated multi-node underwater acoustic target classification task demonstrate the effectiveness of the framework. The results show competitive performance compared to centralized and state-of-the-art multi-view methods, reduced communication costs, and superior robustness in scenarios with node failures.