This paper presents a novel approach to enhance communication efficiency in federated learning through clipped uniform quantization. By leveraging optimal clipping thresholds and client-specific adaptive quantization schemes, the proposed method significantly reduces bandwidth and memory requirements for model weight transmission between clients and the server while maintaining competitive accuracy. We investigate the effects of symmetric clipping and uniform quantization on model performance, emphasizing the role of stochastic quantization in mitigating artifacts and improving robustness. Extensive simulations demonstrate that the method achieves near-full-precision performance with substantial communication savings. Moreover, the proposed approach facilitates efficient weight averaging based on the inverse of the mean squared quantization errors, effectively balancing the trade-off between communication efficiency and model accuracy. Moreover, in contrast to federated averaging, this design obviates the need to disclose client-specific data volumes to the server, thereby enhancing client privacy. Comparative analysis with conventional quantization methods further confirms the efficacy of the proposed scheme.

翻译：本文提出了一种通过截断均匀量化提升联邦学习通信效率的新方法。该方法利用最优截断阈值和客户端自适应的量化方案，在保持模型性能竞争力的同时，显著降低了客户端与服务器之间模型权重传输所需的带宽和内存开销。我们研究了对称截断与均匀量化对模型性能的影响，重点分析了随机量化在减轻量化伪影与提升鲁棒性方面的作用。大量仿真实验表明，该方法在实现显著通信节省的同时，获得了接近全精度的性能。此外，所提方法基于量化误差均方值的倒数进行高效的权重聚合，有效平衡了通信效率与模型精度之间的权衡。与联邦平均方法相比，该设计无需向服务器公开各客户端的数据量信息，从而增强了客户端隐私保护。与传统量化方法的对比分析进一步验证了所提方案的有效性。