Efficient Federated Learning With Adaptive Model Pruning and Quantization Over Wireless Networks

Abstract

Federated learning (FL) enables multiple devices to collaboratively train a global model without sharing local data. However, due to limited local computing capability and communication bandwidth, FL suffers from high learning latency, especially when the model size is large. To address these issues, we propose APQ-FL, an adaptive model pruning and quantization method for wireless FL, to reduce the neural network size and improve communication efficiency. Moreover, device selection and wireless resource allocation are also integrated. We first present a convergence analysis of FL with model pruning and quantized transmission, and then jointly optimize the pruning ratio, quantization bit width, device selection, and wireless bandwidth allocation to minimize the convergence upper bound under latency and bandwidth constraints. We prove that the optimized quantization bit width can be obtained via binary search, and derive the closed-form solutions for the optimal pruning ratio and bandwidth allocation. Subsequently, we propose an efficient device selection strategy and further introduce its fairness-aware extension, APQ-FL-Fair. Experiments show that APQ-FL and APQ-FL-Fair improve test accuracy by 4.96%–15.33% while reducing 23.14%–74.45% communication overhead compared to other methods, and exhibit stable and superior performance even under stringent latency constraints.