arXiv:2601.05613v2 Announce Type: replace Abstract: 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 introduce PiXTime, a novel Transformer-
The proliferation of distributed data sources and increasing privacy concerns necessitate advanced federated learning solutions for effective time series analysis, making this development timely.
This research addresses a critical limitation in federated learning by enabling collaboration on heterogeneous time series data, which is crucial for real-world applications across various industries.
Traditional federated learning's requirement for homogeneous models is relaxed, allowing for more flexible and robust data collaboration across disparate systems without compromising data privacy.
- · Healthcare providers
- · Financial institutions
- · Industrial IoT operators
- · AI/ML developers
- · Centralized data platforms that rely on data pooling
- · Legacy federated learning systems
Increased adoption of federated learning for time series forecasting in sectors with sensitive or distributed data.
Improved predictive accuracy and operational efficiency for entities that can leverage heterogeneous distributed data.
New ethical and regulatory challenges regarding model ownership and bias arising from complex federated architectures.
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Read at arXiv cs.LG