Incremental Model Quantization for Federated Learning in Wireless Networks
Zheng Qin, Gang Feng, YiJing Liu, Xiaoqian Li, Fei Wang, Jun Wang · 2024
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 the limitation of available communication resources which deteriorates FL performance. Some recently proposed model quantization techniques can effectively reduce communication costs by compressing the amount of model data to be transmitted. Unfortunately, conventional quantization methods face difficulties in wireless with rapidly changing 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 gradients of the local model to obtain base gradients as well as incremental gradients and send them to the server. Then the server combines the base and incremental gradients to obtain a more accurate global model where the quantization levels of gradients can be adaptively adjusted according to the instantaneous channel states. Experimental results show our proposed Fed_IQ can significantly reduce transmission delay and improve model accuracy in a wireless network compared with state-of-the-art algorithms.