7 citations · 25 across the 31 of their papers we have counts for
38 papers
Communication-Aware Model Distributed Inference via Latent Representation Compression
Peyman Gholami, Theodoros-Thirimachos Davarakis, Teng Li +9
We study optimization of distributed model inference over resource-constrained edge resources. We propose a framework that optimizes the trade-off between model accuracy and commun…
Efficient and Privacy-Preserving Binary Dot Product via Multi-Party Computation
Fatemeh Jafarian Dehkordi, Elahe Vedadi, Alireza Feizbakhsh +2
Striking a balance between protecting data privacy and enabling collaborative computation is a critical challenge for distributed machine learning. While privacy-preserving techniq…
Model-Distributed Inference for Large Language Models at the Edge
Davide Macario, Hulya Seferoglu, Erdem Koyuncu
We introduce Model-Distributed Inference for Large-Language Models (MDI-LLM), a novel framework designed to facilitate the deployment of state-of-the-art large-language models (LLM…
A Tale of Two Learning Algorithms: Multiple Stream Random Walk and Asynchronous Gossip
Peyman Gholami, Hulya Seferoglu
Although gossip and random walk-based learning algorithms are widely known for decentralized learning, there has been limited theoretical and experimental analysis to understand th…
Priority-Aware Model-Distributed Inference at Edge Networks
Teng Li, Hulya Seferoglu
Distributed inference techniques can be broadly classified into data-distributed and model-distributed schemes. In data-distributed inference (DDI), each worker carries the entire…
Early-Exit meets Model-Distributed Inference at Edge Networks
Marco Colocrese, Erdem Koyuncu, Hulya Seferoglu
Distributed inference techniques can be broadly classified into data-distributed and model-distributed schemes. In data-distributed inference (DDI), each worker carries the entire…