4 papers
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…
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 trade-off between pe…
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…
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…