8 papers
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…
Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks
Xiaoyu Wu, Jiaru Zhang, Yang Hua +4
Few-shot fine-tuning of Diffusion Models (DMs) is a key advancement, significantly reducing training costs and enabling personalized AI applications. However, we explore the traini…
M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction
Jiacheng Lu, Weijian Wang, Mingyuan Xiao +6
Accurately predicting the popularity of micro-videos is a critical but challenging task, characterized by volatile, `rollercoaster-like' engagement dynamics. Existing methods often…
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…
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark
Jianqing Zhang, Yang Liu, Yang Hua +5
Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL)has gain…
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…