Federated Learning is emerging as a privacy-preserving model training approach in distributed edge applications. As such, most edge deployments are heterogeneous in nature i.e., their sensing capabilities and environments vary across deployments. This edge heterogeneity violates the independence and identical distribution (IID) property of local data across clients and produces biased global models i.e. models that contribute to unfair decision-making and discrimination against a particular community or a group. Existing bias mitigation techniques only focus on bias generated from label heterogeneity in non-IID data without accounting for domain variations due to feature heterogeneity and do not address global group-fairness property. Our work proposes a group-fair FL framework that minimizes group-bias while preserving privacy and without resource utilization overhead. Our main idea is to leverage average conditional probabilities to compute a cross-domain group \textit{importance weights} derived from heterogeneous training data to optimize the performance of the worst-performing group using a modified multiplicative weights update method. Additionally, we propose regularization techniques to minimize the difference between the worst and best-performing groups while making sure through our thresholding mechanism to strike a balance between bias reduction and group performance degradation. Our evaluation of human emotion recognition and image classification benchmarks assesses the fair decision-making of our framework in real-world heterogeneous settings.

翻译：联邦学习正成为一种在分布式边缘应用中保护隐私的模型训练方法。然而，大多数边缘部署本质上具有异构性，即它们的感知能力和环境因部署而异。这种边缘异构性违反了各客户端本地数据的独立同分布特性，并产生了有偏见的全局模型，即导致不公平决策和歧视特定社区或群体的模型。现有偏差缓解技术仅关注非独立同分布数据中由标签异质性产生的偏差，未考虑特征异质性导致的域变化，且无法解决全局群体公平性问题。本研究提出了一种群体公平联邦学习框架，该框架在保护隐私且不增加资源开销的前提下最小化群体偏差。我们的核心思想是利用平均条件概率，从异构训练数据中计算出跨域群体\textit{重要性权重}，通过改进的乘性权重更新方法优化表现最差群体的性能。此外，我们提出了正则化技术，以最小化表现最差与最佳群体之间的差距，同时通过阈值机制在偏差减少与群体性能下降之间取得平衡。我们对人类情绪识别和图像分类基准的评估，验证了所提框架在真实异构环境中实现公平决策的能力。