Federated Learning (FL) has achieved significant achievements recently,
enabling collaborative model training on distributed data over edge devices.
Iterative gradient or model exchanges between devices and the centralized
server in the standard FL paradigm suffer from severe efficiency bottlenecks on
the server. While enabling collaborative training without a central server,
existing decentralized FL approaches either focus on the synchronous mechanism
that deteriorates FL convergence or ignore device staleness with an
asynchronous mechanism, resulting in inferior FL accuracy. In this paper, we
propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in
heterogeneous environments with three unique contributions. First, we propose
an asynchronous FL system model with an efficient model aggregation method for
improving the FL convergence. Second, we propose a dynamic staleness-aware
model update approach to achieve superior accuracy. Third, we propose an
adaptive sparse training method to reduce communication and computation costs
without significant accuracy degradation. Extensive experimentation on four
public datasets and four models demonstrates the strength of AEDFL in terms of
accuracy (up to 16.3% higher), efficiency (up to 92.9% faster), and computation
costs (up to 42.3% lower).

我们提出了一种异步高效去中心化联邦学习框架，即 AEDFL，它在异构环境中具有三个独特的贡献：首先，我们提出了一种异步的 FL 系统模型，并使用高效的模型聚合方法来改进 FL 的收敛性；其次，我们提出了一种动态过期感知的模型更新方法，以实现更高的准确性；第三，我们提出了一种自适应的稀疏训练方法，以降低通信和计算成本，同时准确性下降不显著。通过在四个公共数据集和四个模型上进行广泛的实验，证明了 AEDFL 在准确性（高达 16.3%）、效率（高达 92.9%）和计算成本（高达 42.3%）方面的优势。