Search Results for author:
Found 5 papers, 2 papers with code
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible.
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
If the required memory to train a model exceeds this limit, the device will be excluded from the training.
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
To adapt to the devices' heterogeneous resources, CoCoFL freezes and quantizes selected layers, reducing communication, computation, and memory requirements, whereas other layers are still trained in full precision, enabling to reach a high accuracy.
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems
We study the problem of distributed training of neural networks (NNs) on devices with heterogeneous, limited, and time-varying availability of computational resources.
Visual Person Understanding through Multi-Task and Multi-Dataset Learning
We address the problem of learning a single model for person re-identification, attribute classification, body part segmentation, and pose estimation.
