Federated learning (FL) represents a pivotal shift in machine learning (ML) as it enables collaborative training of local ML models coordinated by a central aggregator, all without the need to exchange local data. However, its application on edge devices is hindered by limited computational capabilities and data communication challenges, compounded by the inherent complexity of Deep Learning (DL) models. Model pruning is identified as a key technique for compressing DL models on devices with limited resources. Nonetheless, conventional pruning techniques typically rely on manually crafted heuristics and demand human expertise to achieve a balance between model size, speed, and accuracy, often resulting in sub-optimal solutions. In this study, we introduce an automated federated learning approach utilizing informed pruning, called AutoFLIP, which dynamically prunes and compresses DL models within both the local clients and the global server. It leverages a federated loss exploration phase to investigate model gradient behavior across diverse datasets and losses, providing insights into parameter significance. Our experiments showcase notable enhancements in scenarios with strong non-IID data, underscoring AutoFLIP's capacity to tackle computational constraints and achieve superior global convergence.

翻译：联邦学习（FL）代表了机器学习（ML）领域的关键转变，它允许由中央聚合器协调的本地ML模型进行协同训练，且无需交换本地数据。然而，其在边缘设备上的应用受限于有限的计算能力和数据通信挑战，而深度学习（DL）模型固有的复杂性又加剧了这一问题。模型剪枝被认为是资源受限设备上压缩DL模型的关键技术。然而，传统剪枝技术通常依赖手工设计的启发式规则，并需要人类专业知识来平衡模型大小、速度和准确性，这往往导致次优解。在本研究中，我们提出了一种利用信息剪枝的自动化联邦学习方法，称为AutoFLIP，该方法在本地客户端和全局服务器中动态剪枝和压缩DL模型。它通过联邦损失探索阶段研究模型在多样化数据集和损失函数上的梯度行为，从而提供参数重要性的洞察。我们的实验在强非独立同分布（non-IID）数据场景中展示出显著改进，凸显了AutoFLIP在应对计算约束和实现更优全局收敛方面的能力。