论文2：Federated Learning with Fair Averaging
简介：该论文提出一种基于梯度投影的联邦学习公平性算法（federated fair averaging，简称FedFV)。FedFV探索性地揭示了造成联邦学习公平性的重要因素：大尺度的梯度矛盾差异。该方法充分考虑了不同用户数据集之间的分布差异以及网络状态不稳定带来的掉线挑战，故让服务器得到一个兼顾公平性和准确性的高效模型。论文第一作者是信息学院2020级硕士研究生王铮，通讯作者是信息学院范晓亮高级工程师，合作作者包括澳大利亚墨尔本大学Jianzhong Qi高级讲师等。
One of the main focus in federated learning (FL) is the communication efficiency since a large number of participating edge devices send their updates to the edge server at each round of the model training. Existing works reconstruct each model update from edge devices and implicitly assume that the local model updates are independent over edge devices. In FL, however, the model update is an indirect multi-terminal source coding problem, also called as the CEO problem where each edge device cannot observe directly the gradient that is to be reconstructed at the decoder, but is rather provided only with a noisy version. The existing works do not leverage the redundancy in the information transmitted by different edges. This paper studies the rate region for the indirect multiterminal source coding problem in FL. The goal is to obtain the minimum achievable rate at a particular upper bound of gradient variance. We obtain the rate region for the quadratic vector Gaussian CEO problem under unbiased estimator and derive an explicit formula of the sum-rate-distortion function in the special case where gradient are identical over edge device and dimension. Finally, we analyse communication efficiency of convex Minibatched SGD and non-convex Minibatched SGD based on the sum-rate-distortion function, respectively.