Federated Learning (FL) is a novel machine learning framework, which enables multiple distributed devices cooperatively training a shared model scheduled by a central server while protecting private data locally. However, the non-independent-and-identically-distributed (Non-IID) data samples and frequent communication among participants will slow down the convergent rate and increase communication costs. To achieve fast convergence, we ameliorate the local gradient descend approach in conventional local update rule by introducing the aggregated gradients at each local update epoch, and propose an adaptive learning rate algorithm that further takes the deviation of local parameter and global parameter into consideration at each iteration. The above strategy requires all clients' local parameters and gradients at each local iteration, which is challenging as there is no communication during local update epochs. Accordingly, we utilize mean field approach by introducing two mean field terms to estimate the average local parameters and gradients respectively, which does not require clients to exchange their private information with each other at each local update epoch. Numerical results show that our proposed framework is superior to the state-of-art schemes in model accuracy and convergent rate on both IID and Non-IID dataset.

翻译：联邦学习（FL）是一种新型机器学习框架，允许多个分布式设备在中央服务器调度下协同训练共享模型，同时将私有数据保留在本地。然而，非独立同分布（Non-IID）的数据样本以及参与者之间的频繁通信会降低收敛速度并增加通信成本。为实现快速收敛，我们改进了传统局部更新规则中的局部梯度下降法，在每个局部更新轮次中引入聚合梯度，并提出一种自适应学习率算法，在每次迭代中进一步考虑局部参数与全局参数之间的偏差。上述策略需要在每个局部迭代中获取所有客户端的局部参数和梯度，但由于局部更新轮次期间无通信，这具有挑战性。为此，我们利用平均场方法，引入两个平均场项分别估计平均局部参数和平均梯度，从而无需客户端在每个局部更新轮次中交换彼此私有信息。数值结果表明，我们提出的框架在IID和Non-IID数据集上的模型精度和收敛速度均优于现有最优方案。