4 citations · 4 across the 5 of their papers we have counts for
6 papers · 1 filter
Information-Theoretic Secure Aggregation over Regular Graphs
Xiang Zhang, Zhou Li, Han Yu +4
Large-scale decentralized learning frameworks such as federated learning (FL), require both communication efficiency and strong data security, motivating the study of secure aggreg…
Optimal Rate Region for Multi-server Secure Aggregation with User Collusion
Zhou Li, Xiang Zhang, Kai Wan +3
Secure aggregation is a fundamental primitive in privacy-preserving distributed learning systems, where an aggregator aims to compute the sum of users' inputs without revealing ind…
Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association
Xiang Zhang, Zhou Li, Kai Wan +3
Secure aggregation is motivated by federated learning (FL) where a cloud server aims to compute an {aggregated} model (i.e., weights of deep neural networks) of the locally-trained…
Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion
Xiang Zhang, Kai Wan, Hua Sun +3
Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their su…
The Capacity Region of Information Theoretic Secure Aggregation with Uncoded Groupwise Keys
Kai Wan, Hua Sun, Mingyue Ji +2
This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to a…
On the Information Theoretic Secure Aggregation with Uncoded Groupwise Keys
Kai Wan, Xin Yao, Hua Sun +2
Secure aggregation, which is a core component of federated learning, aggregates locally trained models from distributed users at a central server. The ``secure'' nature of such agg…