Federated Learning

Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning

Tracking IP leakage in federated learning.

A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection

We develop a hybrid federated learning for learning financial-crime predictive models from horizontal and vertical federated data structures.

FedNoisy: A Federated Noisy Label Learning Benchmark

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending …

Revisiting Data-Free Knowledge Distillation with Poisoned Teachers

We uncover the security risk of data-free distillation from a poisoned teacher and propose the first countermeasure.

Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from out-of-distribution (OoD) samples, misclassifying them with high confidence. Recent advances show promising OoD detection …

Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning

Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a set of participating users without requiring raw data to be shared. One major challenge of FL comes from heterogeneity in users, which may have …

Resilient and Communication Efficient Learning for Heterogeneous Federated Systems

The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. However, the heterogeneity of edge network topologies and the uncertainty of wireless transmission are two major …

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

Efficient and federated learning for heterogeneous clients with different memory sizes

Federated Adversarial Debiasing for Fair and Trasnferable Representations

A distributed domain/group debiasing framework for unsupervised domain adaptation or fairness enhancement.

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data. User heterogeneity has imposed significant challenges to FL, which …