Frequency magnitude distribution of noise
Let me try to elaborate a bit on your questions: First, the Wiener-Khintschin-Theorem relates the autocorrelation of a stationary process to its PSD, with the relation of the Fourier Transform, i.e. 
$$\mathcal{F}\{r_{nn}(\tau)\}=S_{nn}(f).$$
The autocorrelation of a signal describes the correlation between two noise samples at distance $\tau$. It is just the second moment of the (joint) distribution of both noise samples. Hence, it does not really tell you something about the actual distribution. However, mostly, the noise is assumed to be Gaussian distributed with zero mean. Then, the autocorrelation completely describes the noise process.

Furthermore, in case you have AWGN, then $r_{nn}(t)=N_0\delta(t)$, i.e. the noise samples are uncorrelated and the PSD is a constant with $S_{nn}(f)=N_0$. 

Your question is: What is the distribution of the noise magnitude in the frequency domain? So, you want to know $Pr(S(f)|_{f_0})$, where $S(f)$ is a realization of the noise process. 

To make this more accessible (at least to me), let me switch to a linear algebra setting (i.e. the finite discrete-time case). The results can be straight-forwardly taken over to the continuos-time case. Let $\vec{n}$ be a realization of the noise process with autocorrelation $R_{nn}=E[\vec{n}\vec{n}^H]$. Let $\mathbf{F}$ be the unitary DFT matrix. So, $\vec{N}=\mathbf{F}\vec{n}$ is one noise realization in the frequency domain. Assuming that $\vec{n}$ consists of Gaussian distributed elements with zero mean, $\vec{N}$ also consists of Gaussian elements (A linear combination of two gaussian random variables remains to be Gaussian). What is the autocorrelation of the frequency-domain noise? It's given by

$$\mathbf{R}_{NN}=\mathbf{F}R_{nn}\mathbf{F}^H.$$

On the diagonal of $R_{NN}$ there is the variance of the Gaussians in the frequency domain. 

So, the distribution of noise in the frequency domain remains to be a Gaussian, with variance given by diagonal on the above expression. I believe the diagonal is actually the PSD that follows from the Wiener-Khintschin Theorem. So, the final answer is:


  The noise in the frequency domain is Gaussian distributed, with zero mean and variance given by the PSD of the noise. (Assuming zero-mean Gaussian time-domain noise)