Selected Publications

For a complete list of my publications, please visit my Google Scholar profile.

[1] Building Trust Beyond Update Divergence: Dual-Refined Aggregation for Byzantine-Robust Federated Learning
Heyi Zhang, X. He, J. Wu, et al.
IEEE Transactions on Information Forensics and Security (TIFS), 2026. [Paper]

Contribution: To address stealthy Byzantine attacks under heterogeneous data distributions in federated learning, we developed a dual-refined aggregation framework that leverages sparse saliency structures for malicious update detection and group-wise robustness-aware weighted aggregation for robust model aggregation.

Technical Expertise: Distributed Learning, Robust Aggregation, Group-wise Robustness, Anomaly Detection, PyTorch, Data Analysis.

[2] Coded Computing Meets Differential Privacy: Byzantine-Robust and Privacy-Preserving Distributed Machine Learning
Y. Xue, J. Wu, X. Lin, Heyi Zhang, et al.
IEEE Transactions on Dependable and Secure Computing (TDSC), 2026. [Paper]

Contribution: To address stragglers and privacy challenges in distributed learning, we developed a Byzantine-robust framework integrating differential privacy with Lagrange coded computing to achieve secure, privacy-preserving, and communication-efficient distributed learning.

Technical Expertise: Differential Privacy, Lagrange Coded Computing, Distributed Programming, Byzantine Fault Tolerance, Straggler Mitigation, Distributed Optimization, PyTorch.

[3] Integrating Blockchain and Deep Learning Into Extremely Resource-Constrained IoT: An Energy-Saving Zero-Knowledge PoL Approach
Heyi Zhang, J. Wu, X. Lin, et al.
IEEE Internet of Things Journal (IoTJ), 2023. [Paper]

Contribution: To address resource and security challenges in resource-constrained IoT, We developed a blockchain-enabled trustworthy distributed learning framework integrating a Zero-Knowledge Proof of Learning (ZPoL) consensus protocol and a quality-aware incentive mechanism.

Technical Expertise: Ethereum Blockchain, Proof of Learning (PoL), Zero-Knowledge Proof (ZKP), Stackelberg Game, Deep Learning, PyTorch, Numpy.

[4] Toward Byzantine-robust Distributed Learning for Sentiment Classification on Social Media Platform
Heyi Zhang, J. Wu, X. Lin, et al.
IEEE Transactions on Computational Social Systems (TCSS), 2024. [Paper]

Contribution: To address Byzantine attacks and straggler issues in distributed machine learning, we developed a Byzantine-robust framework leveraging Lagrange coded computing together with coded-domain similarity-based coded aggregation for Byzantine-robust and efficient distributed model training.

Technical Expertise: Lagrange Coded Computing, Byzantine Fault Tolerance, Cosine similarity, Ethereum Blockchain, Distributed Programming, PyTorch, Numpy, Solidity.

[5] SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
Heyi Zhang, Y. Liu, X. He, et al.
arXiv, 2025. Open-source Benchmark (GitHub, 60+ Stars). [Paper] [GitHub]

Contribution: To systematically benchmark poisoning attacks and defenses in federated learning, we developed and open-sourced FLPoison, a unified, modular, and extensible framework for reproducible robustness evaluation across diverse federated learning algorithms and heterogeneous data settings.

Technical Expertise: Federated Learning, Adversarial Machine Learning, Benchmarking, Robustness Evaluation, Reproducible AI, PyTorch, Pandas.