Communication-Efficient Federated DNN Training: Convert, Compress, Correct

In the federated training of a deep neural network (DNN), model updates are transmitted from the remote users to the parameter server (PS). In many scenarios of practical relevance, one is interested in reducing the communication overhead to enhance training efficiency. To address this challenge, we introduce $\textsf {CO}_{3}$ . $\textsf {CO}_{3}$ takes its name from three processing applied which reduce the communication load when transmitting the local DNN gradients from the remote users to the PS. Namely, 1) gradient quantization through floating-point conversion; 2) lossless compression of the quantized gradient; and 3) correction of quantization error. We carefully design each of the steps above to ensure good training performance under a constraint on the communication rate. In particular, in steps 1) and 2), we adopt the assumption that DNN gradients are distributed according to a generalized normal distribution, which is validated numerically in this article. For step 3), we utilize an error feedback with a memory decay mechanism to correct the quantization error introduced in step 1). We argue that the memory decay coefficient –similar to the learning rate—can be optimally tuned to improve convergence. A rigorous convergence analysis of the proposed $\textsf {CO}_{3}$ with stochastic gradient descent (SGD) is provided. Moreover, with extensive simulations, we show that $\textsf {CO}_{3}$ offers improved performance as compared with existing gradient compression schemes proposed in the literature which employ sketching and nonuniform quantization of the local gradients.