Federated learning across multi-cloud environments faces critical challenges, including non-IID data distributions, malicious participant detection, and substantial cross-cloud communication costs (egress fees). Existing Byzantine-robust methods focus primarily on model accuracy while overlooking the economic implications of data transfer across cloud providers. This paper presents Cost-TrustFL, a hierarchical federated learning framework that jointly optimizes model performance and communication costs while providing robust defense against poisoning attacks. We propose a gradient-based approximate Shapley value computation method that reduces the complexity from exponential to linear, enabling lightweight reputation evaluation. Our cost-aware aggregation strategy prioritizes intra-cloud communication to minimize expensive cross-cloud data transfers. Experiments on CIFAR-10 and FEMNIST datasets demonstrate that Cost-TrustFL achieves 86.7% accuracy under 30% malicious clients while reducing communication costs by 32% compared to baseline methods. The framework maintains stable performance across varying non-IID degrees and attack intensities, making it practical for real-world multi-cloud deployments.

翻译：跨多云环境的联邦学习面临关键挑战，包括非独立同分布数据分布、恶意参与者检测以及高昂的跨云通信成本（如出口费用）。现有的拜占庭鲁棒方法主要关注模型精度，而忽视了跨云服务提供商数据传输的经济影响。本文提出Cost-TrustFL，一种分层联邦学习框架，该框架联合优化模型性能与通信成本，同时提供针对投毒攻击的鲁棒防御。我们提出一种基于梯度的近似沙普利值计算方法，将计算复杂度从指数级降至线性级，从而实现轻量级信誉评估。我们的成本感知聚合策略优先考虑云内通信，以最小化昂贵的跨云数据传输。在CIFAR-10和FEMNIST数据集上的实验表明，与基线方法相比，Cost-TrustFL在30%恶意客户端存在的情况下实现了86.7%的准确率，同时将通信成本降低了32%。该框架在不同非独立同分布程度和攻击强度下均保持稳定性能，使其适用于现实世界的多云部署场景。