3 papers
cs.LG2025
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…
cs.LG2025
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…
cs.LG2024
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…