Xingshuo Han

SIMC 2.0: Improved Secure ML Inference Against Malicious Clients

2022 · arXiv (Cornell University)

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem …

Mind the Cost of Scaffold! Benign Clients May Even Become Accomplices of Backdoor Attack

2024 · arXiv (Cornell University)

By using a control variate to calibrate the local gradient of each client, Scaffold has been widely known as a powerful solution to mitigate the impact of data heterogeneity in Federated Learning. Although Scaffold achieves …

Verifiable and Lightweight Multi-Round Secure Federated Learning

2025 · IEEE Transactions on Dependable and Secure Computing

Federated learning (FL) is a paradigm that ensures the confidentiality and accessibility of data without requiring the collection of private data from multiple sources. It acquires an aggregation model by integrating various local models from …