# On Resilient and Efficient Linear Secure Aggregation in Hierarchical Federated Learning

arXiv: https://arxiv.org/abs/2601.12853 · HTML: https://mlsanity.com/paper/2601.12853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learning](https://mlsanity.com/paper/2601.17995/) (2026)

## Abstract (arXiv)

In this paper, we study the fundamental limits of hierarchical secure aggregation under unreliable communication. We consider a hierarchical network where each client connects to multiple relays, and both client-to-relay and relay-to-server links are intermittent. Under this setting, we characterize the minimum communication and randomness costs required to achieve robust secure aggregation. We then propose an optimal protocol that attains these minimum costs, and establish its optimality through a matching converse proof. In addition, we introduce an improved problem formulation that bridges the gap between existing information-theoretic secure aggregation protocols and practical real-world federated learning problems.