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Cross-silo federated learning achieves up to 40% faster time-to-accuracy when SDN-level network telemetry—rather than client-side heuristics—determines which institutions train synchronously versus asynchronously.
Shielding metal additive manufacturing data doesn't have to mean sacrificing accuracy: prioritizing privacy for less-critical features preserves model performance while ensuring data security.
