Federated Learning (FL) faces major challenges in real-world deployments due to statistical heterogeneity across clients and system heterogeneity arising from resource-constrained devices. While clustering-based approaches mitigate statistical heterogeneity and pruning techniques improve memory and communication efficiency, these strategies are typically studied in isolation. We propose CA-AFP, a unified framework that jointly addresses both challenges by performing cluster-specific model pruning. In CA-AFP, clients are first grouped into clusters, and a separate model for each cluster is adaptively pruned during training. The framework introduces two key innovations: (1) a cluster-aware importance scoring mechanism that combines weight magnitude, intra-cluster coherence, and gradient consistency to identify parameters for pruning, and (2) an iterative pruning schedule that progressively removes parameters while enabling model self-healing through weight regrowth. We evaluate CA-AFP on two widely used human activity recognition benchmarks, UCI HAR and WISDM, under natural user-based federated partitions. Experimental results demonstrate that CA-AFP achieves a favorable balance between predictive accuracy, inter-client fairness, and communication efficiency. Compared to pruning-based baselines, CA-AFP consistently improves accuracy and lower performance disparity across clients with limited fine-tuning, while requiring substantially less communication than dense clustering-based methods. It also shows robustness to different Non-IID levels of data. Finally, ablation studies analyze the impact of clustering, pruning schedules and scoring mechanism offering practical insights into the design of efficient and adaptive FL systems.

翻译：联邦学习（FL）在实际部署中面临两大挑战：客户端间的统计异构性以及资源受限设备导致的系统异构性。基于聚类的方法可缓解统计异构性，而剪枝技术能提升内存与通信效率，但这些策略通常被孤立研究。我们提出CA-AFP，一个通过执行集群特定模型剪枝来共同应对这两大挑战的统一框架。在CA-AFP中，客户端首先被分组为集群，每个集群的模型在训练期间进行自适应剪枝。该框架引入两项关键创新：（1）一种集群感知的重要性评分机制，结合权重幅值、集群内一致性与梯度一致性以识别待剪枝参数；（2）一种迭代剪枝调度策略，在逐步移除参数的同时，通过权重再生实现模型自修复。我们在基于自然用户划分的联邦场景下，使用两个广泛采用的人类活动识别基准数据集（UCI HAR与WISDM）评估CA-AFP。实验结果表明，CA-AFP在预测准确性、客户端间公平性与通信效率之间取得了良好平衡。与基于剪枝的基线方法相比，CA-AFP在有限微调条件下持续提升准确率并降低客户端间的性能差异，同时所需通信量远低于基于密集聚类的方法。该框架还对不同非独立同分布数据程度表现出鲁棒性。最后，消融实验分析了聚类策略、剪枝调度与评分机制的影响，为设计高效自适应的联邦学习系统提供了实践启示。