7 papers · 1 filter
FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization
Hongze Li, Zesheng Zhou, Zhenbiao Cao +3
Traditional Federated Domain Generalization (FedDG) methods focus on learning domain-invariant features or adapting to unseen target domains, often overlooking the unique knowledge…
Perturbation-Induced Linearization: Constructing Unlearnable Data with Solely Linear Classifiers
Jinlin Liu, Wei Chen, Xiaojin Zhang
Collecting web data to train deep models has become increasingly common, raising concerns about unauthorized data usage. To mitigate this issue, unlearnable examples introduce impe…
Bridging Privacy and Robustness for Trustworthy Machine Learning
Xiaojin Zhang, Wei Chen
The widespread adoption of machine learning necessitates robust privacy protection alongside algorithmic resilience. While Local Differential Privacy (LDP) provides foundational gu…
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning
Mingcong Xu, Xiaojin Zhang, Wei Chen +1
Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. Howeve…
FedEAT: A Robustness Optimization Framework for Federated LLMs
Yahao Pang, Xingyuan Wu, Xiaojin Zhang +2
Significant advancements have been made by Large Language Models (LLMs) in the domains of natural language understanding and automated content creation. However, they still face pe…
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
Jialuo He, Wei Chen, Xiaojin Zhang
Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical het…