Energy and Loss-aware Selective Updating for SplitFed Learning with Energy Harvesting-Powered Devices

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.