Federated learning (FL) is a powerful machine learning paradigm which leverages the data as well as the computational resources of clients, while protecting clients' data privacy. However, the substantial model size and frequent aggregation between the server and clients result in significant communication overhead, making it challenging to deploy FL in resource-limited wireless networks. In this work, we aim to mitigate the communication overhead by using quantization. Previous research on quantization has primarily focused on the uplink communication, employing either fixed-bit quantization or adaptive quantization methods. In this work, we introduce a holistic approach by joint uplink and downlink adaptive quantization to reduce the communication overhead. In particular, we optimize the learning convergence by determining the optimal uplink and downlink quantization bit-length, with a communication energy constraint. Theoretical analysis shows that the optimal quantization levels depend on the range of model gradients or weights. Based on this insight, we propose a decreasing-trend quantization for the uplink and an increasing-trend quantization for the downlink, which aligns with the change of the model parameters during the training process. Experimental results show that, the proposed joint uplink and downlink adaptive quantization strategy can save up to 66.7% energy compared with the existing schemes.

翻译：联邦学习（FL）是一种强大的机器学习范式，它利用客户端的数据和计算资源，同时保护客户端的数据隐私。然而，较大的模型规模以及服务器与客户端之间频繁的聚合操作会导致显著的通信开销，这使得在资源受限的无线网络中部署FL具有挑战性。在本工作中，我们旨在通过使用量化来减轻通信开销。先前关于量化的研究主要集中于上行链路通信，采用固定比特量化或自适应量化方法。在本工作中，我们通过联合上下行自适应量化来减少通信开销，提出了一种整体性方法。具体而言，我们在通信能量约束下，通过确定最优的上行和下行量化比特长度来优化学习收敛性。理论分析表明，最优量化水平取决于模型梯度或权重的范围。基于这一洞见，我们提出了适用于上行链路的递减趋势量化和适用于下行链路的递增趋势量化，这与训练过程中模型参数的变化趋势相一致。实验结果表明，与现有方案相比，所提出的联合上下行自适应量化策略最高可节省66.7%的能量。