  Federated learning (FL) scenarios inherently generate a large communication
overhead by frequently transmitting neural network updates between clients and
server. To minimize the communication cost, introducing sparsity in conjunction
with differential updates is a commonly used technique. However, sparse model
updates can slow down convergence speed or unintentionally skip certain update
aspects, e.g., learned features, if error accumulation is not properly
addressed. In this work, we propose a new scaling method operating at the
granularity of convolutional filters which 1) compensates for highly sparse
updates in FL processes, 2) adapts the local models to new data domains by
enhancing some features in the filter space while diminishing others and 3)
motivates extra sparsity in updates and thus achieves higher compression
ratios, i.e., savings in the overall data transfer. Compared to unscaled
updates and previous work, experimental results on different computer vision
tasks (Pascal VOC, CIFAR10, Chest X-Ray) and neural networks (ResNets,
MobileNets, VGGs) in uni-, bidirectional and partial update FL settings show
that the proposed method improves the performance of the central server model
while converging faster and reducing the total amount of transmitted data by up
to 377 times.
