Lipschitz constant of difference of convex functions Let $\mathcal{H}$ be a real Hilbert space and $f:= g-h$ where $g, h \colon \mathcal{H} \to \mathbb{R}$ are continuously differentiable and convex functions with $\lambda-$ and $\mu-$ Lipschitz continuous gradient, respectively.
It's not hard to show that $f$ is $\left( \lambda + \mu \right) -$ Lipschitz continuous gradient. I do not think that it is optimal but I'm not sure how to derive a better constant as well. My guess, it could be $\max \left\lbrace \lambda , \mu \right\rbrace$.

I think you can find it from the Hessian Matrix. I change a bit your notation to include the strong convexity as well. The following holds:
$$ \mu_f I \preceq \nabla^2 f(x) \preceq \lambda_f I, ~ and ~ \mu_g I \preceq \nabla^2 g(x) \preceq \mu_g I, $$
where $\lambda$ denotes the Lipschitz gradient constant and $\mu$ denotes the strong convexity constant. Thus, the Hessian of $h=f-g$ satisfies
$$ (\mu_f - \lambda_g)I \preceq \nabla^2 h(x) \preceq (\lambda_f - \mu_g) I \preceq \lambda_f I \preceq (\lambda_f+\lambda_g) I. $$
So your bound is too conservative as you said.
