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Found 8 papers, 2 papers with code
Accelerated Training on Low-Power Edge Devices
To accelerate training, we propose to jointly adjust the system and application parameters (in our case, the GPU frequency and the batch size of the training task) while adhering to the power constraints on devices.
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices
Federated fine-tuning offers a promising approach for tuning Large Language Models (LLMs) on edge devices while preserving data privacy.
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices
In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing.
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.
