On hierarchical secure aggregation against relay and user collusion
Min Xu, Xuejiao Han, Kai Wan, Gennian Ge

TL;DR
This paper develops and analyzes hierarchical secure aggregation schemes for federated learning, ensuring privacy against collusion with optimal communication load and key size, through theoretical bounds and network function computation techniques.
Contribution
It introduces new bounds and schemes for hierarchical secure aggregation resilient to collusion, optimizing communication and security in federated learning networks.
Findings
Established fundamental limits on communication load for HSA.
Designed collusion-resilient schemes approaching optimal communication efficiency.
Derived key size bounds and achieved them in cyclic network topologies.
Abstract
Secure aggregation (SA) is fundamental to privacy preservation in federated learning (FL), enabling model aggregation while preventing disclosure of individual user updates. This paper addresses hierarchical secure aggregation (HSA) against relay and user collusion in homogeneous networks, where each user connects to relays and each relay serves users. In the two-phase communication framework, users transmit masked data to relays, which then process and forward compiled messages to the server for exact sum recovery. The primary objective is to devise a transmission scheme such that the server can finish the aggregation task, while any group of colluding relays and colluding users cannot reveal any information about the data owned by the non-colluding users. In this study, we establish fundamental limits on the communication load, defined as the ratio of transmitted…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Wireless Communication Security Techniques
