Fully distributed learning schemes such as Gossip Learning (GL) are gaining momentum due to their scalability and effectiveness even in dynamic settings. However, they often imply a high utilization of communication and computing resources, whose energy footprint may jeopardize the learning process, particularly on battery-operated IoT devices. To address this issue, we present Optimized Gossip Learning (OGL)}, a distributed training approach based on the combination of GL with adaptive optimization of the learning process, which allows for achieving a target accuracy while minimizing the energy consumption of the learning process. We propose a data-driven approach to OGL management that relies on optimizing in real-time for each node the number of training epochs and the choice of which model to exchange with neighbors based on patterns of node contacts, models' quality, and available resources at each node. Our approach employs a DNN model for dynamic tuning of the aforementioned parameters, trained by an infrastructure-based orchestrator function. We performed our assessments on two different datasets, leveraging time-varying random graphs and a measurement-based dynamic urban scenario. Results suggest that our approach is highly efficient and effective in a broad spectrum of network scenarios.

翻译：全分布式学习方案（如八卦学习）因其在动态环境中的可扩展性和有效性而日益受到关注。然而，这些方案往往需要大量通信与计算资源，其能源足迹可能危及学习过程，尤其在电池供电的物联网设备上。针对这一问题，我们提出优化八卦学习——一种结合自适应学习过程优化的分布式训练方法，可在实现目标精度的同时最小化学习过程的能耗。我们提出数据驱动的OGL管理方法，通过基于节点接触模式、模型质量及各节点可用资源的实时优化，动态调整每个节点的训练轮次及与邻居交换的模型选择。该方法采用深度神经网络模型对上述参数进行动态调优，该模型由基于基础设施的编排器函数训练。我们在两个不同数据集上进行了评估，利用时变随机图和基于测量的动态城市场景。结果表明，该方法在广泛网络场景中具有高效性和有效性。