SplitFed learning (SFL) is a promising data-privacy preserving decentralized learning framework for IoT devices that has low computation requirement but high communication overhead. To reduce the communication overhead, we present a selective model update method that sends/receives activations/gradients only in selected epochs. However for IoT devices that are powered by harvested energy, the client-side model computations can take place only when the harvested energy can support it. So in this paper, we propose an energy+loss-aware selective updating method for SFL systems where the client-side model is updated based on both the clients’ energy and loss changes. When all clients have the same energy harvesting capability, we show that the proposed method can save energy by 43.7% to 80.5% with 0.5% drop in accuracy compared to an energy-aware method for VGG11 and ResNet20 models on CIFAR-10 and CIFAR-100 datasets. When the energy harvesting capability of the clients are different, the proposed method can save energy by up to 28.8% to 70.0% with 0.5% drop in accuracy.

SplitFed 学习 (SFL) 是一种很有前途的数据隐私保护分散式学习框架，适用于计算要求低但通信开销高的 IoT 设备。为了减少通信开销，我们提出了一种选择性模型更新方法，该方法仅在选定的时期发送/接收激活/梯度。然而，对于由收集的能量供电的物联网设备，只有在收集的能量可以支持它时，客户端模型计算才能进行。因此，在本文中，我们提出了一种 SFL 系统的能量+损失感知选择性更新方法，其中客户端模型根据客户端的能量和损失变化进行更新。当所有客户端具有相同的能量收集能力时，我们表明所提出的方法可以在 0 的情况下节省 43.7% 到 80.5% 的能量。与 CIFAR-10 和 CIFAR-100 数据集上的 VGG11 和 ResNet20 模型的能量感知方法相比，准确度下降了 5%。当客户端的能量收集能力不同时，所提出的方法可以节省高达 28.8% 到 70.0% 的能量，精度下降 0.5%。