This paper introduces an approach to employ clipped uniform quantization in federated learning settings, aiming to enhance model efficiency by reducing communication overhead without compromising accuracy. By employing optimal clipping thresholds and adaptive quantization schemes, our method significantly curtails the bit requirements for model weight transmissions between clients and the server. We explore the implications of symmetric clipping and uniform quantization on model performance, highlighting the utility of stochastic quantization to mitigate quantization artifacts and improve model robustness. Through extensive simulations on the MNIST dataset, our results demonstrate that the proposed method achieves near full-precision performance while ensuring substantial communication savings. Specifically, our approach facilitates efficient weight averaging based on quantization errors, effectively balancing the trade-off between communication efficiency and model accuracy. The comparative analysis with conventional quantization methods further confirms the superiority of our technique.

该论文介绍一种在联邦学习环境中使用剪辑均匀量化的方法，旨在通过减少通信开销而不降低准确性来提高模型效率。通过采用最优剪辑阈值和自适应量化方案，我们的方法显著减少了客户端和服务器之间模型权重传输的比特需求。通过在MNIST数据集上进行广泛的模拟实验，我们的结果表明，所提出的方法实现了近乎全精度的性能，同时确保了实质性的通信节省。具体来说，我们的方法通过基于量化误差的权重平均化实现了高效的权重平均化，有效平衡了通信效率和模型准确性的权衡。与传统的量化方法的比较分析进一步证实了我们的技术的优越性。

通信高效的剪辑均匀量化器用于联邦学习