Robust Aggregation in Over-the-Air Computation with Federated Learning: A Semantic Anti-Interference Approach

Over-the-air federated learning (AirFL) enables distributed model training across wireless edge devices, preserving data privacy and minimizing bandwidth usage. However, challenges such as channel noise, non-identically distributed data, limited computational resources, and small local datasets lead to distorted model updates, inconsistent global models, increased training latency, and overfitting, all of which reduce accuracy and efficiency. To address these issues, we propose the Semantic Anti-Interference Aggregation (SAIA) framework, which integrates a semantic autoencoder, component-wise median aggregation, validation accuracy weighting, and data augmentation. First, a semantic autoencoder compresses model parameters into low-dimensional vectors, maintaining high signal quality and reducing communication costs. Second, component-wise median aggregation minimizes noise and outlier impact, ideal for AirFL as it avoids mean-based aggregation’s noise sensitivity and complex methods’ high computation. Third, validation accuracy weighting aligns updates from non-identically distributed data to ensure consistent global models. Fourth, data augmentation doubles dataset sizes, mitigating overfitting and reducing variance. Experiments on MNIST demonstrate that SAIA achieves an accuracy of approximately 96% and a loss of 0.16, improving accuracy by 3.3% and reducing loss by 39% compared to conventional federated learning approaches. With reduced computational and communication overhead, SAIA ensures efficient training on resource constrained IoT devices.