# Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learning

arXiv: https://arxiv.org/abs/2601.17995 · HTML: https://mlsanity.com/paper/2601.17995/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Quantized Federated Learning under Transmission Delay and Outage Constraints](https://mlsanity.com/paper/2106.09397/) (2021)
- [Communication-Computation Efficient Gradient Coding](https://mlsanity.com/paper/1802.03475/) (2018)
- [Relay-Assisted Cooperative Federated Learning](https://mlsanity.com/paper/2107.09518/) (2021)
- [Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association](https://mlsanity.com/paper/2503.04564/) (2025)
- [Heterogeneity-aware Gradient Coding for Straggler Tolerance](https://mlsanity.com/paper/1901.09339/) (2019)
- [Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations](https://mlsanity.com/paper/2210.02235/) (2022)
- [Coding for Straggler Mitigation in Federated Learning](https://mlsanity.com/paper/2109.15226/) (2021)
- [Amplify-and-Forward Relaying for Hierarchical Over-the-Air Computation](https://mlsanity.com/paper/2007.14730/) (2020)
- [Optimal Communication-Computation Trade-Off in Heterogeneous Gradient Coding](https://mlsanity.com/paper/2103.01589/) (2021)
- [Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks](https://mlsanity.com/paper/2406.03766/) (2024)
- [Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation](https://mlsanity.com/paper/2403.14905/) (2024)
- [Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise](https://mlsanity.com/paper/2501.14644/) (2025)
- [Cooperative Gradient Coding](https://mlsanity.com/paper/2507.05230/) (2025)
- [On Resilient and Efficient Linear Secure Aggregation in Hierarchical Federated Learning](https://mlsanity.com/paper/2601.12853/) (2026)

## Abstract (arXiv)

Hierarchical federated learning (HFL) has emerged as an effective paradigm to enhance link quality between clients and the server. However, ensuring model accuracy while preserving privacy under unreliable communication remains a key challenge in HFL, as the coordination among privacy noise can be randomly disrupted. To address this limitation, we propose a robust hierarchical secure aggregation scheme, termed H-SecCoGC, which integrates coding strategies to enforce structured aggregation. The proposed scheme not only ensures accurate global model construction under varying levels of privacy, but also avoids the partial participation issue, thereby significantly improving robustness, privacy preservation, and learning efficiency. Both theoretical analyses and experimental results demonstrate the superiority of our scheme under unreliable communication across arbitrarily strong privacy guarantees