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Differential privacy doesn't have to mean sacrificing prediction accuracy: this work shows how to achieve tighter, more efficient conformal prediction sets under DP constraints by avoiding data splitting.
DFL gets a communication and privacy boost: PaME exchanges only sparse coordinates, achieving linear convergence under milder assumptions than existing methods.
