[
    {
        "id": "osp-21043",
        "type": "article-journal",
        "title": "Understanding Trajectory Heterogeneity in Federated World Model Learning",
        "author": [
            {
                "family": "Wei",
                "given": "Yipan"
            },
            {
                "family": "Yan",
                "given": "Zhaokun"
            },
            {
                "family": "Hong",
                "given": "Ziming"
            },
            {
                "family": "Wu",
                "given": "Jiaqi"
            },
            {
                "family": "Wang",
                "given": "Lixu"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/understanding-trajectory-heterogeneity-in-federated-world-model-learning",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use distributed records, while ownership boundaries within a trajectory restrict the examples each client can construct. Our study benchmarks this cross-time setting through hourly action-conditioned clinical prediction on eight MIMIC-IV disease cohorts, comprising 40.87 million transition memberships. We specify severity-based client ownership, patient-separated construction, local history and future-window rules, and paired rollout evaluation from one to 32 hours. A matrix of ten federated algorithms covers 32 disease--partition configurations under five rounds of ten-percent participation. Three findings emerge from existing results and training logs. First, client ownership and participation jointly restrict long-window coverage: only 7.55\\%--21.36\\% of pooled-available 32-step windows have a locally complete anchor visited during training, averaged across diseases. Second, finer severity partitions accompany higher FedAvg error in 15 of 16 paired comparisons, while algorithm gains are small and horizon-dependent: FedProx reduces mean error by 0.56\\%, with no consistent improvement at 32 steps. Third, algorithm labels conceal distinct update behavior, including inactive extrapolation and orders-of-magnitude differences in update scale. Cached-update performance also varies strongly across trajectory partitions under the same benchmark protocol. These results establish temporal access, participation coverage, optimization behavior, and horizon-resolved prediction as complementary dimensions for evaluating federated clinical world models."
    }
]