Secure Embedding Aggregation for Federated Representation Learning

ISIT(2023)

引用 0|浏览59
暂无评分
摘要
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 SecEA, which leverages all potential aggregation opportunities among all the clients, while providing privacy guarantees for the set of local entities and corresponding embeddings simultaneously at each client, against a curious server and up to T < N/2 colluding clients.
更多
查看译文
关键词
federated representation learning,aggregation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要