Enhancing the Performance of Model Pruning in Over-the-Air Federated Learning with Non-IID Data

This paper focuses on over-the-air federated learning (OTA-FL) for edge devices that have non-independent and identically distributed (non-IID) datasets. Federated averaging (FedAvg), the vanilla FL algorithm does not perform well with non-IID data, leading to reduced global model performance. Pruning, a deep neural network model compression technique can reduce model size to be compatible with low-complexity devices without a significant impact on accuracy. However, inclusion of pruning and channel impairments can significantly degrade the performance of OTA-FL networks in a non-IID setup. We therefore first investigate and quantify this pruning-accuracy trade-off when edge devices utilize an OTA-FL setup - and find significant degradation for pruning rates over 50%. We then investigate two approaches to mitigate this degradation. The first is to utilize FedProx, an effective federated aggregation algorithm designed to improve performance on edge devices with non-IID data. The second approach is to iteratively re-train the model at the parameter server (PS) using a limited dataset representative of the task being learned across the edge devices. This approach is suited for cases where it is possible to access such data, e.g. keyboard prediction, several computer vision and natural language processing tasks. We conduct thorough simulations to assess the performance and convergence behavior of both approaches, for different pruning levels and datasets. Results show that considerable gains can be achieved with these approaches, particularly when pruning is not too aggressive and for less complex classification tasks. Further research is however needed to develop algorithms that are more robust to high pruning, non-IID data distributions, and OTA aggregation at low signal to noise ratios (SNRs).