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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,3,3]]},"abstract":"<jats:p>Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm that leads to sub-optimal model accuracy and higher training overhead. In this paper, we challenge the prevailing notion of partial-model aggregation and propose a novel \"full-weight aggregation\" method named Moss, which aggregates all weights within heterogeneous models to preserve comprehensive knowledge. 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