Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients. To address the former challenge, we introduce the federated-centralized concordance property and show that the federated single-mini-batch training approach can achieve comparable performance as the corresponding centralized training in the Non-IID environments. To deal with the latter, we present the federated multi-mini-batch approach and illustrate that it can establish a trade-off between the performance and communication efficiency and outperforms federated averaging in the Non-IID settings.
翻译:联邦学习在业绩和网络通信方面面临挑战,特别是在数据不独立、用户分布不均的环境中,为了应对前一个挑战,我们引入了联邦中央协调财产,并表明联邦单小批培训方法可以作为非国际学习环境的相应集中培训实现可比业绩。为了应对后一个问题,我们介绍了联邦多小批培训方法,并表明它可以在业绩和通信效率之间实现权衡,并在非国际学习发展环境中实现超过联邦平均业绩。