Decentralized Federated Learning (DFL) has become popular due to its robustness and avoidance of centralized coordination. In this paradigm, clients actively engage in training by exchanging models with their networked neighbors. However, DFL introduces increased costs in terms of training and communication. Existing methods focus on minimizing communication often overlooking training efficiency and data heterogeneity. To address this gap, we propose a novel \textit{sparse-to-sparser} training scheme: DA-DPFL. DA-DPFL initializes with a subset of model parameters, which progressively reduces during training via \textit{dynamic aggregation} and leads to substantial energy savings while retaining adequate information during critical learning periods. Our experiments showcase that DA-DPFL substantially outperforms DFL baselines in test accuracy, while achieving up to $5$ times reduction in energy costs. We provide a theoretical analysis of DA-DPFL's convergence by solidifying its applicability in decentralized and personalized learning. The code is available at:https://github.com/EricLoong/da-dpfl

翻译：去中心化联邦学习因其鲁棒性及避免集中协调的特点而广受欢迎。在该范式中，客户端通过与网络邻居交换模型积极参与训练。然而，去中心化联邦学习在训练和通信方面引入了更高的成本。现有方法主要关注最小化通信成本，却常忽略训练效率和数据异质性。为弥补这一不足，我们提出了一种新颖的"稀疏到更稀疏"训练方案：DA-DPFL。该方案初始化时仅使用部分模型参数，并通过"动态聚合"在训练过程中逐步减少参数数量，从而在关键学习阶段保留足够信息的同时显著降低能耗。实验表明，DA-DPFL在测试准确率上大幅优于去中心化联邦学习基线方法，同时实现了高达5倍的能耗降低。我们提供了DA-DPFL收敛性的理论分析，验证了其在去中心化与个性化学习中的适用性。代码地址：https://github.com/EricLoong/da-dpfl