471 citations · 2.2k across the 114 of their papers we have counts for
9 papers · 1 filter
Governing AI Forgetting: Auditing for Machine Unlearning Compliance
Qinqi Lin, Ningning Ding, Lingjie Duan +1
Despite legal mandates for the right to be forgotten, AI operators routinely fail to comply with data deletion requests. While machine unlearning (MU) provides a technical solution…
FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
Kangkang Sun, Jun Wu, Minyi Guo +2
Federated Learning (FL) enables collaborative model training without data sharing, yet participants face a fundamental challenge, e.g., simultaneously ensuring fairness across demo…
Provably Convergent Federated Trilevel Learning
Yang Jiao, Kai Yang, Tiancheng Wu +2
Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine le…
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
Pengchao Han, Xingyan Shi, Jianwei Huang
Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model paramete…
Optimization Design for Federated Learning in Heterogeneous 6G Networks
Bing Luo, Xiaomin Ouyang, Peng Sun +3
With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still a…
Cross-Silo Federated Learning: Challenges and Opportunities
Chao Huang, Jianwei Huang, Xin Liu
Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private. Based o…
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…
Cost-Effective Federated Learning in Mobile Edge Networks
Bing Luo, Xiang Li, Shiqiang Wang +2
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server…
Cost-Effective Federated Learning Design
Bing Luo, Xiang Li, Shiqiang Wang +2
Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its pract…