5 papers
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…
Root Cause Analysis of Anomalies in 5G RAN Using Graph Neural Network and Transformer
Antor Hasan, Conrado Boeira, Khaleda Papry +3
The emergence of 5G technology marks a significant milestone in developing telecommunication networks, enabling exciting new applications such as augmented reality and self-driving…
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…
A Calibrated and Automated Simulator for Innovations in 5G
Conrado Boeira, Antor Hasan, Khaleda Papry +3
The rise of 5G deployments has created the environment for many emerging technologies to flourish. Self-driving vehicles, Augmented and Virtual Reality, and remote operations are e…