Efficient Federated Learning in Wireless Networks With Incremental Model Quantization and Uploading

Abstract

Federated Learning (FL) has been widely recognized as a promising promoter for future intelligent wireless networks, by collaboratively training a global machine learning (ML) model in a privacy-preserving manner. However, the transmission of large-scale models between clients and servers is susceptible to limited communication resources. Recently proposed model quantization can reduce communication costs by compressing the amount of model data to be transmitted. These methods need to be modified when used in wireless networks with rapidly changing radio channels. In this paper, we propose a federated learning scheme with incremental model quantization and uploading mechanism, called Fed_IQ. Specifically, individual clients quantize the local model parameters to derive the base and incremental model parameters. The base model is first uploaded, while the incremental model is uploaded when the wireless link is sufficiently good. The quantization levels are also adapted to the instantaneous channel states. The server then uses only the base model or combines the base and incremental model to aggregate a more accurate global model. Experimental results show our proposed Fed_IQ can significantly reduce transmission delay and improve model accuracy in a wireless network compared with a number of known state-of-the-art algorithms.