MobiFLow: Datasets and Benchmarks for Federated Learning in Real-world Mobility

Recent advances in Federated Learning (FL) have established it as a promising paradigm for real-world domains where privacy, regulatory constraints, or data ownership concerns prevent centralized data collection, such as mobility. Despite growing interests in tasks including traffic or passenger flow prediction, the empirical evaluation of FL strategies in the mobility domain remains limited. Due to privacy concerns, existing studies often rely on proprietary datasets, hindering reproducibility, while publicly available mobility datasets are rarely designed for federated settings. To address these limitations, we introduce MobiFLow, an open-source federated dataset suite for mobility applications built upon natural partitions, i.e., splits derived from intrinsic dataset properties. The suite comprises seven datasets spanning diverse mobility tasks, modalities, and scales, covering both cross-device and cross-silo FL scenarios. MobiFLow is complemented by standardized preprocessing, baseline models, benchmarking utilities, and practical evaluation guidelines to support reproducible research. Its modular design allows researchers to seamlessly integrate new datasets and federated learning strategies within a unified framework. We provide baseline results using widely adopted FL strategies and show that realistic mobility heterogeneity makes collaboration far from uniformly beneficial: local training is often surprisingly strong, pooled training can be competitive when client distributions are relatively homogeneous, and no general-purpose FL method consistently dominates across datasets, underscoring the need for realistic benchmarks and mobility-aware FL methods. By releasing datasets, code, and benchmarking tools, MobiFLow aims to promote reproducible research and establish a common ground for the systematic evaluation and fair comparison of FL methods in mobility.