Federated learning enables multiple medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains a major challenge. Many existing works attempt to address this issue by leveraging model representations (e.g., mean feature vectors) to correct local training; however, they often face two key limitations: 1) Incomplete Contextual Representation Learning: Current approaches primarily focus on final-layer features, overlooking critical multi-level cues and thus diluting essential context for accurate segmentation. 2) Layerwise Style Bias Accumulation: Although utilizing representations can partially align global features, these methods neglect domain-specific biases within intermediate layers, allowing style discrepancies to build up and reduce model robustness. To address these challenges, we propose FedBCS to bridge feature representation gaps via domain-invariant contextual prototypes alignment. Specifically, we introduce a frequency-domain adaptive style recalibration into prototype construction that not only decouples content-style representations but also learns optimal style parameters, enabling more robust domain-invariant prototypes. Furthermore, we design a context-aware dual-level prototype alignment method that extracts domain-invariant prototypes from different layers of both encoder and decoder and fuses them with contextual information for finer-grained representation alignment. Extensive experiments on two public datasets demonstrate that our method exhibits remarkable performance.

To describe how advanced engineering students decide their post-baccalaureate plans, we conducted a mixed-methods study with engineering students at a large public university in the Midwest. According to the surveys, there were no statistically significant differences between men and women in their choices of post-baccalaureate plans. Students who had positive undergraduate research experiences and students who felt attached to their departments as undergraduates were more likely to enter graduate school immediately after graduation. Students who had industrial internships were more likely to enter professional practice immediately. From the interview data, we defined eight decision style archetypes that characterize how students make post-baccalaureate plans.