8 papers
Multi-Bitwidth Quantization for LLMs Using Additive Codebooks
Liza Babaoglu, Shuangyi Chen, Ashish Khisti
As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively manage the performance-efficien…
Black-Box Detection of LLM-Generated Text Using Generalized Jensen-Shannon Divergence
Shuangyi Chen, Ashish Khisti
We study black-box detection of machine-generated text under practical constraints: the scoring model (proxy LM) may mismatch the unknown source model, and per-input contrastive ge…
Secure Aggregation in Federated Learning using Multiparty Homomorphic Encryption
Erfan Hosseini, Shuangyi Chen, Ashish Khisti
A key operation in federated learning is the aggregation of gradient vectors generated by individual client nodes. We develop a method based on multiparty homomorphic encryption (M…
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
Shuangyi Chen, Yuanxin Guo, Yue Ju +3
Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) optimize federated training by reducing computational and communication costs. We propose RoLoRA, a f…
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA
Shuangyi Chen, Yue Ju, Hardik Dalal +2
Parameter-Efficient Fine-Tuning (PEFT) has risen as an innovative training strategy that updates only a select few model parameters, significantly lowering both computational and m…
Secure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption
Shuangyi Chen, Yue Ju, Zhongwen Zhu +1
We propose a secure inference protocol for a distributed setting involving a single server node and multiple client nodes. We assume that the observed data vector is partitioned ac…
SECO: Secure Inference With Model Splitting Across Multi-Server Hierarchy
Shuangyi Chen, Ashish Khisti
In the context of prediction-as-a-service, concerns about the privacy of the data and the model have been brought up and tackled via secure inference protocols. These protocols are…
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning
Shuangyi Chen, Anuja Modi, Shweta Agrawal +1
Vertical federated learning (VFL) enables the collaborative training of machine learning (ML) models in settings where the data is distributed amongst multiple parties who wish to…