How to feed key-value features (aggregated data) to LSTM?
Sounds to me like you can reshape the input of a timestep as matrix s.t. $X_t \in \mathbb{R}^{n \times 2}$ (assuming you only have a and b as features) and use Convolutional LSTM layers:
$t=0$:
$X_0 = \begin{bmatrix}a(0,0)&amp;b(0,0)\\a(0,1)&amp;b(0,1)\\\vdots&amp;\vdots\\a(0,n)&amp;b(0,n)\end{bmatrix}$
$t=m$:
$X_m = \begin{bmatrix}a(m,0)&amp;b(m,0)\\\vdots&amp;\vdots\\a(m,n)&amp;b(m,n)\end{bmatrix}$
Basically each sequential keys $y$ is a row in your input matrix. You can think of each matrix as some kind of image you feed to your LSTM network.
Hope that helps you any further.