Design and Implementation of Intelligent Collaborative Algorithm for Distributed Network Learning

Distributed network learning in edge environments faces critical challenges including device heterogeneity, communication bottlenecks, and frequent node disconnections. To overcome these limitations, this paper introduces an intelligent collaborative algorithm with three innovations: (1) A three-layer hierarchical architecture (local learning, relay coordination, global aggregation) decouples tasks across heterogeneous devices; (2) An LSTM-GCN hybrid model enables topology-aware gradient compression, dynamically sparsifying updates based on real-time bandwidth/latency predictions; (3) An asynchronous double-buffer mechanism with local snapshot buffering maintains training continuity during node failures. Evaluations on non-IID MNIST/CIFAR-10 datasets demonstrate 42% faster convergence than FedAvg, 60% lower communication overhead versus DSGD, and sustained 87.5% accuracy at 50% disconnection rates. The framework significantly enhances robustness and efficiency in dynamic edge networks while minimizing synchronization delays and resource contention.