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Ramin Khalili

2 accepted papers

2023

Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices

NeurIPS 2023poster

Federated learning (FL) is usually performed on resource-constrained edge devices, e.g., with limited memory for the computation. If the required memory to train a model exceeds this limit, the device will be excluded from the training. This can lead to a lower accuracy as valuable data and computat…

2022

DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems

AAAI 2022technical

We study the problem of distributed training of neural networks (NNs) on devices with heterogeneous, limited, and time-varying availability of computational resources. We present an adaptive, resource-aware, on-device learning mechanism, DISTREAL, which is able to fully and efficiently utilize the a…

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