Federated Learning (FL) solves many of this decade's concerns regarding data privacy and computation challenges. FL ensures no data leaves its source as the model is trained at where the data resides. However, FL comes with its own set of challenges. The communication of model weight updates in this distributed environment comes with significant network bandwidth costs. In this context, we propose a mechanism of compressing the weight updates using Autoencoders (AE), which learn the data features of the weight updates and subsequently perform compression. The encoder is set up on each of the nodes where the training is performed while the decoder is set up on the node where the weights are aggregated. This setup achieves compression through the encoder and recreates the weights at the end of every communication round using the decoder. This paper shows that the dynamic and orthogonal AE based weight compression technique could serve as an advantageous alternative (or an add-on) in a large scale FL, as it not only achieves compression ratios ranging from 500x to 1720x and beyond, but can also be modified based on the accuracy requirements, computational capacity, and other requirements of the given FL setup.

本文提出一种基于自编码器（Autoencoders）的动态正交压缩权重更新的方法，该方法不仅可以在大规模联邦学习中实现高达500x至1720x等多种压缩比，而且还可以根据精度要求、计算容量等要求进行修改，从而成为一种有优势的替代或补充方式。

使用自编码压缩权重更新在大规模联邦学习中进行通信优化