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

ArXi:2601.05613v2 Announce Type: replace-cross While collaborative forecasting on distributed time series is highly desirable, directly pooling localized datasets is often impractical due to data sharing constraints. Federated learning offers a promising alternative, yet conventional federated learning algorithms require homogeneous model architectures, which are incompatible with the structural discrepancies, such as unaligned temporal resolutions and mismatched variable channels, commonly observed across decentralized nodes. To bridge this gap, we.