Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. The network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.

翻译：编码计算是一种利用纠删码技术来缓解集中式计算网络中计算节点掉队问题的方法。联邦学习是一种分布式训练模型，用于处理分布在客户端设备上的训练数据。在本研究中，我们提出了一种近似聚合数据矩阵求逆的方法，其中数据由客户端生成，这与联邦学习范式类似，同时也能抵抗掉队节点。为此，我们基于梯度编码提出了一种编码计算方法。我们对该方法进行了改进，使得协调器在任何时候都无法访问本地数据，而客户端则可以访问聚合矩阵以完成其任务。我们所考虑的网络并非集中管理，且通信过程能够抵御潜在的窃听者。