Quantization is a common approach to mitigate the communication cost of federated learning (FL). In practice, the quantized local parameters are further encoded via an entropy coding technique, such as Huffman coding, for efficient data compression. In this case, the exact communication overhead is determined by the bit rate of the encoded gradients. Recognizing this fact, this work deviates from the existing approaches in the literature and develops a novel quantized FL framework, called \textbf{r}ate-\textbf{c}onstrained \textbf{fed}erated learning (RC-FED), in which the gradients are quantized subject to both fidelity and data rate constraints. We formulate this scheme, as a joint optimization in which the quantization distortion is minimized while the rate of encoded gradients is kept below a target threshold. This enables for a tunable trade-off between quantization distortion and communication cost. We analyze the convergence behavior of RC-FED, and show its superior performance against baseline quantized FL schemes on several datasets.

本研究解决了联邦学习中通信成本过高的问题，提出了一种名为RC-FED的新框架，该框架在量化梯度时考虑了保真度和数据速率约束。通过将量化失真最小化，同时保持编码梯度的速率低于目标阈值，显示出该方法在多个数据集上相较于基准量化联邦学习方案的优越性能。

约束速率的量化用于通信高效的联邦学习