Investigating Energy Efficiency and Performance Trade-offs in LLM Inference Across Tasks and DVFS Settings

no code implementations • 14 Jan 2025 • Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic

To that end, in this work, we investigate the effect of important parameters on the performance and energy efficiency of LLMs during inference and examine their trade-offs.

Benchmarking Question Answering +1

FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN

no code implementations • 25 Mar 2024 • Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic

Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e. g., mobile devices, IoT edge nodes).

Federated Learning Privacy Preserving