mlsanityData to 7 Oct 2026

Hierarchical Secure Aggregation with Heterogeneous Security Constraints and Arbitrary User Collusion

arXiv 2507.14768 ↗First posted 19 Jul 2025cs.IT cs.CR cs.DCcited by 2

Not on the map. Why?

Abstract (arXiv)

In hierarchical secure aggregation (HSA), a server communicates with clustered users through an intermediate layer of relays to compute the sum of users' inputs under two security requirements -- server security and relay security. Server security requires that the server learns nothing beyond the desired sum even when colluding with a subset of users, while relay security requires that each relay remains oblivious to the users' inputs under collusion. Existing work on HSA enforces homogeneous security where titall inputs must be protected against titany subset of potential colluding users with sizes up to a predefined threshold. Such a homo formulation cannot capture scenarios with tithet secty reqs where diff users may demand various levels of protection. In this paper, we study hierarchical secure aggregation (HSA) with heterogeneous security requirements and arbitrary user collusion. Specifically, we consider scenarios where the inputs of certain groups of users must remain information-theoretically secure against inference by the server or any relay, even if the server or any relay colludes with an arbitrary subset of other users. Under server security, the server learns nothing about these protected inputs beyond the prescribed aggregate sum, despite any such collusion. Under relay security, each relay similarly obtains no information about the protected inputs under the same collusion model. We characterize the optimal communication rates achievable across all layers for all parameter regimes. Furthermore, we study the minimum source keys required at the users to ensure security. For this source key requirement, we provide tight characterizations in two broad regimes determined by the security and collusion constraints, and establish a general information-theoretic lower bound together with a bounded-gap achievable scheme for the remaining regime.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 19 Jul 2025.

Builds on

12 of 33 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  6. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  7. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  8. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  9. 2021Information Theoretic Secure Aggregation with User Dropoutscited by 19
  10. 2022SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guaranteescited by 14
  11. 2025Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Associationcited by 7
  12. 2023Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusioncited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.