2^ddCt can indicate the fold-change "treated vs. control" or its reciprocal (fold change "controls vs. treated"), depending on how the ddCt is calculated.

Assuming iddCt was calculated as dCt[treated] - dCt[control] and the dCt values ware calculated as Ct[reference]-Ct[targetgene], then 2^ddCt would in fact indicate the fold-change "treated vs. control" (Note that Livak & Schmittgen strangely supposed a reciprocal calculation, so that 2^(-ddCt) is the fold-change "treatmen vs. control")

In this case, 2^ddCt = 0.1 means that the target gene expression level in the treated sample is about 0.1 (=10%) of that in the control sample. You can write that the treatment reduced the expression level to 10% or by 90%.

I strongly advice against "down-fold change" measures. Thats mathematically flawed and creates more confusion than it helps. I think it's nuch clearer to say that the expression levels are "reduced to x%" or "reduced by x%".

Typically, such estimates are very uncertain (it is enlightnig to have a look at the confidence intervals!), and one is often happy that the data is sufficient to conclude the mere direction of the regulation (that is, that the data is just "significant" under H0: ddCt=0). It may give quite a false impression then to state these (point-)estimates. It's typically ok just to give the direction (up or down) and to indicate that the data warrants that conclusion (small p-value). If you need to give a quantitative value, consider evaluating the confidence interval (example, made up: "the treatment reduced the gene expression levels to 3%-56% (95% confidence interval)" or, alternatively: ""the treatment reduced the gene expression levels by 44%-97% (95% confidence interval)").

If you need the quantities to compare effects of different treatments, then you should rather fit a model including a coefficient for the difference of the treatment effects and determine the significance for the null that the difference in effects is zero. Only with this you will see if your data warrants the conclusion about the sign of the difference, and is you cannot make a confident conclusion, there is no point to calculate and report all the estimates (it distracts and possibly misguides the reader, implying conclusions about differences that are not supported by the data!).

If fold change is coming in decimal value, it means that your target gene expression has reduced.

In case of 0.1, do it as -1/0.1 you will get answer as -10 which means 10 fold expression has been reduced compared to the normal.

For every value coming in decimal, you can calculate downregulate expression by divding -0.1 by it.

the thing you should have to consider is that what condition you had taken to calculate delta delta Ct. If it is control it mean it is downregulated in comparison to control.

because control fold change is normalized as 1.00

if you get the fold change value 0.1 it indicates 10 times downregulation of that gene in respect to control.

Representation methods may be different but it would indicate the same result.