Using featureCount -M is not the best way to account for multi-mapping reads, its default (counting multiple times) is a rather crude way of counting multi-mapped reads, and with option -M --fraction, it is a rather naive way of splitting the counts of multi-mapping reads. If you really need to quantify multi-mappers, use RSEM / Salmon / kallisto.
With RSEM and Salmon, you may align with STAR using --quantMode TranscriptomeSAM --quantTranscriptomeBan IndelSoftclipSingleend, quantify and then summarize the transcript counts to gene counts with tximport.
With Salmon and kallisto, you can quantify directly against a reference transcriptome and then summarize the transcript counts to gene counts with tximport.
what do you think if I do the quantification at -g exon-id instead of -g gene-id, (because it interests me only the exons )
You want to summarize to read counts on exons? Or do you want to only count read overlapping exons, but summarize to read counts on genes? Why only exons? But if only exons interest you, then it is fine to use -g exon-id - I just don't understand why you need it.