5 papers
ADCA: Attention-Driven Multi-Party Collusion Attack in Federated Self-Supervised Learning
Jiayao Wang, Yiping Zhang, Jiale Zhang +4
Federated Self-Supervised Learning (FSSL) integrates the privacy advantages of distributed training with the capability of self-supervised learning to leverage unlabeled data, show…
HPE: Hallucinated Positive Entanglement for Backdoor Attacks in Federated Self-Supervised Learning
Jiayao Wang, Yang Song, Zhendong Zhao +5
Federated self-supervised learning (FSSL) enables collaborative training of self-supervised representation models without sharing raw unlabeled data. While it serves as a crucial p…
Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
ZiHeng Huang, Di Wu, Jun Bai +4
Machine unlearning is critical for enforcing data deletion rights like the "right to be forgotten." As a decentralized paradigm, Federated Learning (FL) also requires unlearning, b…
Datasets for Fairness in Language Models: An In-Depth Survey
Jiale Zhang, Zichong Wang, Avash Palikhe +2
Despite the growing reliance on fairness benchmarks to evaluate language models, the datasets that underpin these benchmarks remain critically underexamined. This survey addresses…
IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learning
Jiayao Wang, Yang Song, Zhendong Zhao +4
Federated self-supervised learning (FSSL) combines the advantages of decentralized modeling and unlabeled representation learning, serving as a cutting-edge paradigm with strong po…