ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2503.04564
63
0

Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association

6 March 2025
Xiang Zhang
Zhou Li
Kai Wan
Hua Sun
Mingyue Ji
Giuseppe Caire
ArXivPDFHTML
Abstract

Secure aggregation is motivated by federated learning (FL) where a cloud server aims to compute an averaged model (i.e., weights of deep neural networks) of the locally-trained models of numerous clients, while adhering to data security requirements. Hierarchical secure aggregation (HSA) extends this concept to a three-layer network, where clustered users communicate with the server through an intermediate layer of relays. In HSA, beyond conventional server security, relay security is also enforced to ensure that the relays remain oblivious to the users' inputs (an abstraction of the local models in FL). Existing study on HSA assumes that each user is associated with only one relay, limiting opportunities for coding across inter-cluster users to achieve efficient communication and key generation. In this paper, we consider HSA with a cyclic association pattern where each user is connected to BBB consecutive relays in a wrap-around manner. We propose an efficient aggregation scheme which includes a message design for the inputs inspired by gradient coding-a well-known technique for efficient communication in distributed computing-along with a highly nontrivial security key design. We also derive novel converse bounds on the minimum achievable communication and key rates using information-theoretic arguments.

View on arXiv
@article{zhang2025_2503.04564,
  title={ Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association },
  author={ Xiang Zhang and Zhou Li and Kai Wan and Hua Sun and Mingyue Ji and Giuseppe Caire },
  journal={arXiv preprint arXiv:2503.04564},
  year={ 2025 }
}
Comments on this paper