  Efficiently running federated learning (FL) on resource-constrained devices
is challenging since they are required to train computationally intensive deep
neural networks (DNN) independently. DNN partitioning-based FL (DPFL) has been
proposed as one mechanism to accelerate training where the layers of a DNN (or
computation) are offloaded from the device to the server. However, this creates
significant communication overheads since the intermediate activation and
gradient need to be transferred between the device and the server during
training. While current research reduces the communication introduced by DNN
partitioning using local loss-based methods, we demonstrate that these methods
are ineffective in improving the overall efficiency (communication overhead and
training speed) of a DPFL system. This is because they suffer from accuracy
degradation and ignore the communication costs incurred when transferring the
activation from the device to the server. This article proposes EcoFed - a
communication efficient framework for DPFL systems. EcoFed eliminates the
transmission of the gradient by developing pre-trained initialization of the
DNN model on the device for the first time. This reduces the accuracy
degradation seen in local loss-based methods. In addition, EcoFed proposes a
novel replay buffer mechanism and implements a quantization-based compression
technique to reduce the transmission of the activation. It is experimentally
demonstrated that EcoFed can reduce the communication cost by up to 133x and
accelerate training by up to 21x when compared to classic FL. Compared to
vanilla DPFL, EcoFed achieves a 16x communication reduction and 2.86x training
time speed-up. EcoFed is available from https://github.com/blessonvar/EcoFed.

Distributed, Parallel, and Cluster Computing