CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning

2026-02-25

AI Summary

This work addresses the limitations of existing quantum secure aggregation (QSA) schemes, which rely on global GHZ states that are difficult to scale and lack effective mechanisms for identifying Byzantine clients. To overcome these challenges, the authors propose a clustered QSA framework that introduces a hierarchical structure into quantum secure aggregation: clients are partitioned into groups, and local quantum aggregation is performed within each cluster using small-scale GHZ states. Malicious nodes are identified by detecting anomalous clusters through statistical measures such as cosine similarity and Euclidean distance. This approach significantly enhances system scalability, quantum state fidelity, and Byzantine robustness while remaining compatible with near-term quantum hardware constraints. Moreover, the method ensures stable model convergence under depolarizing noise.

Technology Category

• Machine Learning: Quantum Machine Learning
• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Secure aggregation schemes have been proposed to mitigate these attacks. In this work, we aim to understand how these techniques are implemented in quantum-assisted FL. Quantum Secure Aggregation (QSA) has been proposed, offering information-theoretic privacy by encoding client updates into the global phase of multipartite entangled states. Existing QSA protocols, however, rely on a single global Greenberger-Horne-Zeilinger (GHZ) state shared among all participating clients. This design poses fundamental challenges: fidelity of large-scale GHZ states deteriorates rapidly with the increasing number of clients; and (ii) the global aggregation prevents the detection of Byzantine clients. We propose Clustered Quantum Secure Aggregation (CQSA), a modular aggregation framework that reconciles the physical constraints of near-term quantum hardware along with the need for Byzantine-robustness in FL. CQSA randomly partitions the clients into small clusters, each performing local quantum aggregation using high-fidelity, low-qubit GHZ states. The server analyzes statistical relationships between cluster-level aggregates employing common statistical measures such as cosine similarity and Euclidean distance to identify malicious contributions. Through theoretical analysis and simulations under depolarizing noise, we demonstrate that CQSA ensures stable model convergence, achieves superior state fidelity over global QSA.

Problem

• Byzantine-robustness
• Quantum Secure Aggregation
• Federated Learning
• GHZ states
• Secure aggregation

Innovation

• Quantum Secure Aggregation
• Byzantine Robustness
• Clustered Aggregation
• GHZ States
• Federated Learning