Secure Embedding Aggregation for Federated Representation Learning. (arXiv:2206.09097v2 [cs.LG] UPDATED)

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 …

aggregation assistance clients data distributed entities framework key network private private data representation server social social network the key train under