用量子退火解决联邦学习中的恶意客户端筛选问题，提升对复杂攻击的检测能力。
Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
- 将客户端选择建模为QUBO问题，联合优化所有组合以找最接近的群体。
- 在15个客户端时对高级欺骗攻击检测准确率达95.11%（MNIST）和97.78%（CIFAR-10）。
- 提出多信号集成策略，使系统在100客户端下仍保持95.3%平均检测率。
联邦学习在保护数据隐私的同时于分散客户端间训练全局模型，但大规模下易受恶意更新影响。传统拜占庭鲁棒聚合方法如MultiKrum通过计算梯度与最近邻的距离评分，可能遗漏保持诚实数据统计特性的恶意更新。本文提出一种基于量子退火的客户端选择方法，将客户端选择重构为无约束二次二值优化（QUBO）问题，将成对距离编码为代价函数，由量子退火器求解。与MultiKrum的逐客户端贪心评分不同，QUBO联合优化所有子集，寻找相互最接近的m个客户端。在小规模（15客户端）下，对最具挑战性的拜占庭攻击表现更优：例如在MNIST上高级欺骗攻击检测准确率达95.11%（对比MultiKrum的81.33%），在CIFAR-10上达97.78%（对比75.56%）。在简单攻击中表现较差，说明二者互补。随着客户端数量增加，QUBO性能下降。为此，本文引入多信号集成框架，基于欧氏与余弦Krum得分差构建双特征路由门，将攻击分为四类，并将逃避攻击引导至带惩罚项的QUBO与共识投票机制。在100客户端的MNIST上，多信号方法平均检测准确率达95.3%，优于经典MultiKrum的91.8%，在稀疏欺骗（72.0%→95.2%，+23.2点）和高级欺骗（80.4%→85.2%，+4.8点）上提升显著。结果表明，结合多信号的QUBO量子退火是应对复杂拜占庭策略的有效且可扩展的防御方案。
原文摘要 · Abstract (English)
Federated Learning (FL) trains a global model across decentralized clients while preserving data privacy, but at scale it is vulnerable to malicious updates. Byzantine-resilient aggregation methods such as MultiKrum score gradients against their nearest neighbors and can miss malicious updates that preserve the statistical properties of honest ones. We propose a quantum annealing approach that reformulates client selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem, encoding pairwise distances into a cost function solved by quantum annealers (QA). Unlike MultiKrum's greedy per-client scoring, the QUBO formulation jointly optimizes over all subsets to find the mutually closest group of $m$ clients. At small scale (15 clients), QUBO outperforms MultiKrum on the most challenging Byzantine attacks: e.g., Advanced LIE is detected with 95.11% accuracy versus 81.33% on MNIST and 97.78% versus 75.56% on CIFAR-10. QUBO fares poorly on simpler attacks where MultiKrum excels, so the two methods are complementary. QUBO quality also degrades as the number of clients grows. To address this, we introduce a MultiSignal ensemble that uses a dual-feature routing gate based on Euclidean and cosine Krum score gaps to classify attacks into four regimes and routes evasion attacks to a suspicion-penalized QUBO with agreement voting. At 100 clients on MNIST, MultiSignal achieves 95.3% average detection accuracy versus 91.8% for classical MultiKrum, with the largest gains on Sparse Lie (72.0% to 95.2%, +23.2 points) and Advanced Lie (80.4% to 85.2%, +4.8 points). These results show that QUBO-based quantum annealing with MultiSignal is a principled and scalable defense against the most challenging Byzantine strategies in federated learning.
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