Search Results for author:
Found 5 papers, 1 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.
Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary
We consider online scheduling on unrelated (heterogeneous) machines in a speed-oblivious setting, where an algorithm is unaware of the exact job-dependent processing speeds.
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.
Distributed Learning on Heterogeneous Resource-Constrained Devices
We consider a distributed system, consisting of a heterogeneous set of devices, ranging from low-end to high-end.
