Secure Embedding Aggregation for Federated Representation Learning

Jiaxiang Tang; Jinbao Zhu; Songze Li; Lichao Sun

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arXiv · 2206.09097

Secure Embedding Aggregation for Federated Representation Learning

Jiaxiang Tang·Jinbao Zhu·Songze Li·Lichao Sun

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Abstract

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.

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Jiaxiang Tang, Jinbao Zhu, Songze Li, Lichao Sun. 2022-06-18. Secure Embedding Aggregation for Federated Representation Learning. https://arxiv.org/abs/2206.09097

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