1 citations · 2 across the 6 of their papers we have counts for
6 papers
Mobile Backscatter Communication for the Battery-less Internet of Things
Weining Song, Thiemo Voigt, Stefanos Kaxiras +2
We enable backscatter communication in the battery-less mobile Internet of Things (IoT). Backscatter communication is extensively studied in static settings. Existing designs are,…
FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation
Shenghui Li, Thiemo Voigt
Federated fine-tuning with Low-Rank Adaptation (LoRA) enables efficient collaborative adaptation of Large Language Models (LLMs) without centralizing private data. However, LoRA's…
Who Needs DRAM? We Have Fiber
Hannah Atmer, Yuan Yao, Thiemo Voigt +1
The rising pressure on DRAM availability and contract pricing reflects generative AI's massive high-performance memory requirements. This pressure is heavily compounded by hypersca…
Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling
Hannah Atmer, Yuan Yao, Thiemo Voigt +1
Energy consumption dictates the cost and environmental impact of deploying Large Language Models. This paper investigates the impact of on-chip SRAM size and operating frequency on…
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning
Shenghui Li, Edith C. -H. Ngai, Fanghua Ye +1
Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising paradigm for privacy-preserving and efficient adaptation of Pre-trained Language Models (PLMs) in Fed…
Synergizing Foundation Models and Federated Learning: A Survey
Shenghui Li, Fanghua Ye, Meng Fang +4
Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models…