Distributed time series data presents a challenge for federated learning, as clients often possess different feature sets and have misaligned time steps. Existing federated time series models are limited by the assumption of perfect temporal or feature alignment across clients. In this paper, we propose FedTDD, a novel federated time series diffusion model that jointly learns a synthesizer across clients. At the core of FedTDD is a novel data distillation and aggregation framework that reconciles the differences between clients by imputing the misaligned timesteps and features. In contrast to traditional federated learning, FedTDD learns the correlation across clients' time series through the exchange of local synthetic outputs instead of model parameters. A coordinator iteratively improves a global distiller network by leveraging shared knowledge from clients through the exchange of synthetic data. As the distiller becomes more refined over time, it subsequently enhances the quality of the clients' local feature estimates, allowing each client to then improve its local imputations for missing data using the latest, more accurate distiller. Experimental results on five datasets demonstrate FedTDD's effectiveness compared to centralized training, and the effectiveness of sharing synthetic outputs to transfer knowledge of local time series. Notably, FedTDD achieves 79.4% and 62.8% improvement over local training in Context-FID and Correlational scores.

翻译：分布式时间序列数据对联邦学习提出了挑战，因为客户端通常具有不同的特征集且时间步未对齐。现有的联邦时间序列模型受限于客户端间时间或特征完全对齐的假设。本文提出FedTDD，一种新颖的联邦时间序列扩散模型，可在客户端间联合学习合成器。FedTDD的核心是一个创新的数据蒸馏与聚合框架，通过填补未对齐的时间步和特征来协调客户端间的差异。与传统联邦学习不同，FedTDD通过交换局部合成输出而非模型参数来学习客户端时间序列间的相关性。协调器通过交换合成数据利用客户端共享知识，迭代改进全局蒸馏器网络。随着蒸馏器随时间不断优化，它随后提升客户端局部特征估计的质量，使每个客户端能利用最新、更精确的蒸馏器改进其局部缺失数据填补。在五个数据集上的实验结果表明，FedTDD相比集中式训练具有显著有效性，且共享合成输出能有效传递局部时间序列知识。值得注意的是，FedTDD在Context-FID和相关性分数上分别比局部训练提升了79.4%和62.8%。