Algorithm 2.

FADE Server Aggregation

Input: Initial models f, G, D momentum parameter β
1:	for t ∈ 1, …, Tmax do
2:	Select m active users uniformly at random into $\mathcal{A}$
3:	Broadcast θt = (ft, Gt, Dt) users
4:	for user i in $\mathcal{A}$ in parallel do
5:	User updates by Algorithm 1
6:	Aggregate ${\left\{{\theta }_t^k=\left(f_t^i,G_t^i,D_t^i\right)\right\}}_{i=1}^m$ and average
	${\theta }_{t+1}\leftarrow \beta {\sum }_{i=1}^m\frac{n_i}{N}{\theta }_t^i+(1-\beta ){\theta }_t$
	Output: ft, Gt, Dt