Addressing Non-IID Data in Federated Learning with Dual Attention Mechanism for Edge Computing Applications

Résumé fourni par la source

In edge intelligence scenarios, Federated Learning (FL) has emerged as a preeminent method for ensuring privacy preservation and fostering collaboration among end devices. This is primarily due to its ability to train a global model in an edge computing environment without compromising the data privacy of end devices. However, challenges arise in the form of non-independent and identically distributed (Non-IID) issues, such as heterogeneous graph data distributions, stemming from heterogeneity in end device data. These issues notably impede the convergence speed of the federated model and escalate communication costs. To mitigate these issues, we introduce a novel edge computing based FL framework with a Dual Attention mechanism, termed FedDAD. Specifically, FedDAD integrates a Graph Attention Network (GAT) within the local model training process, which is designed to capture the variety of relationships across diverse client data distributions. Concurrently, FedDAD employs an attention mechanism across different client models to fuse key features from various client models into the global model. The efficacy of FedDAD is evaluated through extensive experiments on three heterogeneous graph datasets, demonstrating its superior performance over representative FL baselines.

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