Federated learning in mobile edge systems faces a key challenge: how to train models efficiently while still allowing slow devices to participate. Many existing methods either drop slow devices to speed up training, which hurts fairness, or wait for all devices, which wastes the computing power and time of faster ones. CLAMP addressed this through dynamic depth selection, allowing each client to train an adjusted number of model layers based on their capabilities. However, CLAMP's minimum depth aggregation creates a bottleneck. The server only aggregates the layers trained by all clients, which means that updates from deeper layers trained by faster devices are ignored. This strategy leaves the model capacity unused and limits the convergence speed. We propose Mask-Aware CLAMP, which replaces minimum-depth aggregation with layer-wise mask-aware aggregation. MA-CLAMP aggregates each model layer independently using only the clients that trained it, subject to minimum participation thresholds that ensure stability. This approach preserves deeper layer updates from capable clients while maintaining CLAMP's adaptive depth selection and straggler handling. MA-CLAMP does not require changes to client-side training. Experiments across MNIST, Fashion-MNIST, and CIFAR-10 with 100 heterogeneous clients show that MA-CLAMP achieves faster convergence on moderately complex tasks, improved resource efficiency in computation and energy use, maintains or improves model accuracy across all datasets, and enhances slow-device inclusion rates. MA-CLAMP provides a more effective solution for production federated learning systems where model quality, resource efficiency, and fair client participation are all essential.
Joy Okolo (Sun,) studied this question.