Project

EcoFed: Efficient Communication for DNN Partitioning-based Federated Learning

EcoFed is a communication-efficient framework for DNN partitioning-based federated learning (DPFL) systems that reduces the need to transfer gradients by initializing pre-trained models on devices, improving accuracy and reducing communication overhead. It introduces a novel replay buffer mechanism and uses quantization-based compression to minimize activation data transmission between devices and servers - Github

FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

FedAdapt is a holistic framework for an IoT-edge environment that surmounts the challenges of accelerating federated learning on resource constrained devices, reducing the impact of stragglers arising from computational heterogeneity of IoT devices and adapting to varying network bandwidth between devices and an edge server - Github

EasyQuant: Post-training Quantization via Scale Optimization

EasyQuant(EQ) is an efficient and simple post-training quantization method via effectively optimizing the scales of weights and activations - Github