Federated learning is a machine learning training paradigm that enables
clients to jointly train models without sharing their own localized data.
However, the implementation of federated learning in practice still faces
numerous challenges, such as the large communication overhead due to the
repetitive server-client synchronization and the lack of adaptivity by
SGD-based model updates. Despite that various methods have been proposed for
reducing the communication cost by gradient compression or quantization, and
the federated versions of adaptive optimizers such as FedAdam are proposed to
add more adaptivity, the current federated learning framework still cannot
solve the aforementioned challenges all at once. In this paper, we propose a
novel communication-efficient adaptive federated learning method (FedCAMS) with
theoretical convergence guarantees. We show that in the nonconvex stochastic
optimization setting, our proposed FedCAMS achieves the same convergence rate
of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts. Extensive
experiments on various benchmarks verify our theoretical analysis.

本文介绍了一种新的通信高效的自适应联邦学习方法 FedCAMS，该方法具有理论收敛保证并在各种基准测试上进行了广泛实验验证。