Large deviations principle for partial sums $U$-processes

The large deviations principle (LDP) is known to hold for $U$-statistics of real-valued kernel functions of i.i.d. random variables, where appropriate exponential tail conditions are assumed to hold. We prove that these conditions suffice for the large deviations principle to carry over to the partial sums $U$-processes corresponding to the statistics.