Time series are highly valuable and rarely shareable across nodes, making federated learning a promising paradigm to leverage distributed temporal data. However, different sampling standards lead to diverse time granularities and variable sets across nodes, hindering classical federated learning. We propose PiXTime, a novel time series forecasting model designed for federated learning that enables effective prediction across nodes with multi-granularity and heterogeneous variable sets. PiXTime employs a personalized Patch Embedding to map node-specific granularity time series into token sequences of a unified dimension for processing by a subsequent shared model, and uses a global VE Table to align variable category semantics across nodes, thereby enhancing cross-node transferability. With a transformer-based shared model, PiXTime captures representations of auxiliary series with arbitrary numbers of variables and uses cross-attention to enhance the prediction of the target series. Experiments show PiXTime achieves state-of-the-art performance in federated settings and demonstrates superior performance on eight widely used real-world traditional benchmarks.

翻译：时间序列具有极高价值且难以在节点间共享，这使得联邦学习成为利用分布式时序数据的一种前景广阔的范式。然而，不同的采样标准导致各节点间存在多样化的时间粒度和变量集，阻碍了经典联邦学习的应用。我们提出PiXTime，一种专为联邦学习设计的新型时间序列预测模型，它能够在具有多粒度和异构变量集的节点间实现有效预测。PiXTime采用个性化的Patch Embedding，将节点特定粒度的时间序列映射到统一维度的令牌序列，以供后续共享模型处理；并使用全局VE Table来对齐节点间的变量类别语义，从而增强跨节点的可迁移性。通过一个基于Transformer的共享模型，PiXTime能够捕捉具有任意数量变量的辅助序列的表征，并利用交叉注意力机制来增强目标序列的预测。实验表明，PiXTime在联邦学习设置下取得了最先进的性能，并在八个广泛使用的真实世界传统基准测试中展现出优越的性能。