Secure Embedding Aggregation for Federated Representation Learning

• URL: http://arxiv.org/abs/2206.09097v2
• Date: Thu, 4 May 2023 05:45:34 GMT
• Title: Secure Embedding Aggregation for Federated Representation Learning
• Authors: Jiaxiang Tang, Jinbao Zhu, Songze Li, Lichao Sun
• Abstract summary: A group of $N$ distributed clients train collaboratively over their private data, for the representations 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. We provide privacy guarantees for the set of local entities and corresponding embeddings emphsimultaneously at each client, against a curious server and up to $T N/2$ colluding clients.
• Score: 21.601284171303213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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