The difference between RPKM and FPKM is generally going to be much less than the difference between RPKM and RPKM for two different experiments with different methodologies. That is to say, since one is in RPKM and the other FPKM, the processing was probably different and thus they are not safe to compare regardless of the units. I recommend you try to get a hold of the original data and do the processing identically if you wish to compare results. Even then there may be other factors out of your control (wet-lab protocols) that make them incomparable.

You cannot convert between RPKM and FPKM, but in most cases they are extremely similar so if that were the only difference I doubt it would be significant.

In theory RPKM should just be twice FPKM, but that is unlikely to actually be the case, as is noted by the other commenters, differences in processing protocol, are likely to mean they are not comparable, and would not be even if they were both FPKM or both RPKM.

You might be able to get some meaning from the dataset if you are just doing presence/absence calls, but I wouldn't use a 0 threshold. Instead convert your values to TPM (transcripts per million) but normalising each value to the sum of expression for that sample and multiplying by 1 million. E.g. if I had RPKMs from 4 genes of 0, 1, 5 and 4 the TPMs would be (0x10^6)/10, (110^6)/10, (510^6)/10 and (410^6)/10. Transcripts Per Million is much easier to reason with and more comparable between samples. As a very approximate rule of thumb, a human cell has around 200,000 mRNAs in it at any one time, so a TPM of 5 corresponds to one molecule per cell and can serve as a handy rule of thumb for calling present/absent.

Be careful though as differences in processing might lead to false positive differences - e.g. if one set was processed allowing multimapping and the other not, then you will be a present call for paralogs in one sample and an absent call in the other, irrespective of whether there is actually an expression difference.

As already mentioned, 2*RPKM should be equal to FPKM , if you are dealing with paired end reads in both cases.

FPKM/RPKM was intended as a approximate concentration value. Within an experiment, you can make comparisons between transcripts. Between experiments you should rather use TPM for transcripts or raw counts for genes (these vales are usually normalised with the tools like DESeq2 or edgeR).

Moreover, if you use different types of software to compute your bad numbers, comparisons will become even harder. Cufflinks uses an internal length normalisation and coverage based assumptions which you have to take into consideration when comparing.

Try to get the raw data from your collaborator, or at least the alignments.

Cheers

Michael