6 papers · 1 filter
One Analyst Is Not Ground Truth: Grading Agent-Built Financial Models Against Observed Professional Practice
Jiacheng Lu, Sinuo Wang, Yiming Li +15
Language model agents can now construct complete financial models, but it remains unclear whether they have learned the financial judgment that gives those models meaning. Existing…
FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
Peishen Yan, Yang Hua, Hao Wang +4
Federated fine-tuning of large language models (LLMs) with low-rank adaptation (LoRA) offers a communication-efficient and privacy-preserving solution for task-specific adaptation.…
POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learning
Kuai Yu, Xiaoyu Wu, Peishen Yan +6
Federated Learning (FL) enables decentralized model training across multiple clients without exposing local data, but its distributed feature makes it vulnerable to backdoor attack…
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
Jiaru Zhang, Rui Ding, Qiang Fu +6
Causal discovery is a structured prediction task that aims to predict causal relations among variables based on their data samples. Supervised Causal Learning (SCL) is an emerging…
THOR: A Generic Energy Estimation Approach for On-Device Training
Jiaru Zhang, Zesong Wang, Hao Wang +8
Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and…
Poisoning with A Pill: Circumventing Detection in Federated Learning
Hanxi Guo, Hao Wang, Tao Song +4
Without direct access to the client's data, federated learning (FL) is well-known for its unique strength in data privacy protection among existing distributed machine learning tec…