4 papers
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…
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…
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks
Nikolaos Koursioumpas, Lina Magoula, Ioannis Stavrakakis +3
Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Pre…