113 citations · 123 across the 8 of their papers we have counts for
8 papers
Accelerated Training on Low-Power Edge Devices
Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Heba Khdr +3
Training on edge devices poses several challenges as these devices are generally resource-constrained, especially in terms of power. State-of-the-art techniques at the device level…
Efficient Zeroth-Order Federated Finetuning of Language Models on Resource-Constrained Devices
Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Ramin Khalili +2
Federated Learning (FL) is a promising paradigm for finetuning Large Language Models (LLMs) across distributed data sources while preserving data privacy. However, finetuning such…
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices
Kilian Pfeiffer, Mohamed Aboelenien Ahmed, Ramin Khalili +1
In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require m…
Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators
Kilian Pfeiffer, Konstantinos Balaskas, Kostas Siozios +1
In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do…
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
Kilian Pfeiffer, Martin Rapp, Ramin Khalili +1
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…
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Kilian Pfeiffer, Ramin Khalili, Jörg Henkel
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 th…
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
Kilian Pfeiffer, Martin Rapp, Ramin Khalili +1
Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources. However, in synchronous FL, all devices need to fini…
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems
Martin Rapp, Ramin Khalili, Kilian Pfeiffer +1
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 a…