Federated Learning (FL) has emerged as a transformative approach in healthcare, enabling collaborative model training across decentralized data sources while preserving user privacy. However, performance of FL rapidly degrades in practical scenarios due to the inherent bias in non Independent and Identically distributed (non-IID) data among participating clients, which poses significant challenges to model accuracy and generalization. Therefore, we propose the Bias-Aware Client Selection Algorithm (BACSA), which detects user bias and strategically selects clients based on their bias profiles. In addition, the proposed algorithm considers privacy preservation, fairness and constraints of wireless network environments, making it suitable for sensitive healthcare applications where Quality of Service (QoS), privacy and security are paramount. Our approach begins with a novel method for detecting user bias by analyzing model parameters and correlating them with the distribution of class-specific data samples. We then formulate a mixed-integer non-linear client selection problem leveraging the detected bias, alongside wireless network constraints, to optimize FL performance. We demonstrate that BACSA improves convergence and accuracy, compared to existing benchmarks, through evaluations on various data distributions, including Dirichlet and class-constrained scenarios. Additionally, we explore the trade-offs between accuracy, fairness, and network constraints, indicating the adaptability and robustness of BACSA to address diverse healthcare applications.

翻译：联邦学习（FL）已成为医疗领域的一种变革性方法，能够在保护用户隐私的前提下，利用分散的数据源进行协同模型训练。然而，在实际场景中，由于参与客户端之间的数据具有非独立同分布（non-IID）的固有偏置，FL的性能会迅速下降，这对模型的准确性和泛化能力构成了重大挑战。为此，我们提出了偏置感知客户端选择算法（BACSA），该算法能够检测用户偏置，并根据其偏置特征策略性地选择客户端。此外，所提算法考虑了隐私保护、公平性以及无线网络环境的约束，使其适用于服务质量（QoS）、隐私和安全性至关重要的敏感医疗应用。我们的方法首先提出了一种新颖的用户偏置检测方法，通过分析模型参数并将其与特定类别的数据样本分布相关联来实现。随后，我们利用检测到的偏置以及无线网络约束，构建了一个混合整数非线性客户端选择问题，以优化FL性能。通过在多种数据分布（包括狄利克雷分布和类别约束场景）上进行评估，我们证明与现有基准方法相比，BACSA能够提升收敛速度和准确性。此外，我们还探讨了准确性、公平性与网络约束之间的权衡，表明BACSA在应对多样化医疗应用时具有适应性和鲁棒性。