In this work, we explored federated learning in temporal heterogeneity across
clients. We observed that global model obtained by \texttt{FedAvg} trained with
fixed-length sequences shows faster convergence than varying-length sequences.
We proposed methods to mitigate temporal heterogeneity for efficient federated
learning based on the empirical observation.

本研究探讨了在客户端之间存在的时间异质性的联邦学习，并观察到使用固定长度序列进行训练的 FedAvg 获得的全局模型比使用不同长度序列的模型收敛速度更快。我们提出了一种基于经验观察的方法来减轻时间异质性，以实现高效的联邦学习。

时序异质性中的联邦学习

Federated Learning in Temporal Heterogeneity

Robust prediction of citywide traffic flows at different time periods plays a
crucial role in intelligent transportation systems. While previous work has
made great efforts to model spatio-temporal correlations, existing methods
still suffer from two key limitations: i) Most models collectively predict all
regions' flows without accounting for spatial heterogeneity, i.e., different
regions may have skewed traffic flow distributions. ii) These models fail to
capture the temporal heterogeneity induced by time-varying traffic patterns, as
they typically model temporal correlations with a shared parameterized space
for all time periods. To tackle these challenges, we propose a novel
Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework
which enhances the traffic pattern representations to be reflective of both
spatial and temporal heterogeneity, with auxiliary self-supervised learning
paradigms. Specifically, our ST-SSL is built over an integrated module with
temporal and spatial convolutions for encoding the information across space and
time. To achieve the adaptive spatio-temporal self-supervised learning, our
ST-SSL first performs the adaptive augmentation over the traffic flow graph
data at both attribute- and structure-levels. On top of the augmented traffic
graph, two SSL auxiliary tasks are constructed to supplement the main traffic
prediction task with spatial and temporal heterogeneity-aware augmentation.
Experiments on four benchmark datasets demonstrate that ST-SSL consistently
outperforms various state-of-the-art baselines. Since spatio-temporal
heterogeneity widely exists in practical datasets, the proposed framework may
also cast light on other spatial-temporal applications. Model implementation is
available at this https URL.

本文提出了一种新的 Spatio-Temporal Self-Supervised Learning (ST-SSL) 交通预测框架以解决现有方法中存在的空间异质性和时间异质性问题，其中结合了时间和空间卷积以及自我监督辅助任务，实验结果表明，该方法在四个基准数据集上表现良好而且可适用于其他时空应用。