Robust Distributed Cooperative Classification with Learned Compressed-Feature Diffusion

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.