Algorithm 1.

FADE User Update

Input: f, G, D received from server, learning rate η, adversarial parameter λ, user data distribution pi
1:	f0, D0, G0 = f, D, G
2:	for t = 1, …, K do
3:	Sample a batch by x ~ pi (x) or (x, y) ~ pi(x, y)
4:	z ← G(x)
5:	${\nabla }_f\leftarrow \frac{\partial L_i^{\text{task}}}{\partial f}$, ${\nabla }_D\leftarrow \frac{\partial L_i^{\text{adv}}}{\partial D}$
6:	if adversarial game D is accepted by user i then
7:	${\nabla }_G\leftarrow \frac{\partial z}{\partial G}\left(\frac{\partial L_i^{\text{task}}}{\partial z}+\lambda \frac{\partial L_i^{\text{adv}}}{\partial z}\right)$
8:	Dt+1 ← Dt + η∇D
9:	Gt+1 ← Gt − η∇G
10:	else
11:	${\nabla }_G\leftarrow \frac{\partial z}{\partial G}\frac{\partial L_i^{\text{task}}}{\partial z}$
12:	Dt+1 ← Dt
13:	Gt+1 ← Gt − η∇G
14:	ft+1 ← ft − η∇f
	Output: fK+1, GK+1, DK+1