  We consider a federated representation learning framework, where with the
assistance of a central server, a group of $N$ distributed clients train
collaboratively over their private data, for the representations (or
embeddings) of a set of entities (e.g., users in a social network). Under this
framework, for the key step of aggregating local embeddings trained privately
at the clients, we develop a secure embedding aggregation protocol named
\scheme, which leverages all potential aggregation opportunities among all the
clients, while providing privacy guarantees for the set of local entities and
corresponding embeddings \emph{simultaneously} at each client, against a
curious server and up to $T < N/2$ colluding clients.
