PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data Structures Across Nodes

• URL: http://arxiv.org/abs/2601.05613v1
• Date: Fri, 09 Jan 2026 08:11:45 GMT
• Title: PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data Structures Across Nodes
• Authors: Yiming Zhou, Mingyue Cheng, Hao Wang, Enhong Chen,
• Abstract summary: Time series are highly valuable and rarely shareable across nodes.<n>Different sampling standards lead to diverse time granularities and variable sets across nodes.<n>We propose PiXTime, a novel time series forecasting model designed for federated learning.
• Score: 52.821072802825654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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