Tuo Zhang's Homepage

Ph.D. Student in Electrical Engineering
University of Southern California

Los Angeles, USA

tuozhang [at] usc.edu

Tuo Zhang is a Ph.D. student in the EE department at the University of Southern California. Currently, he is advised by Prof. Salman Avestimehr. Before joining USC, he received his Bachelor of Science with High Honor from the University of California, Santa Barbara under the supervision of Prof. Mahnoosh Alizadeh and Prof. Forrest Brewer. His research now focuses on distributed/federated machine learning algorithms, systems, and applications.

Publications

[10] FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
Samiul Alam, Tuo Zhang, Tiantian Feng, Hui Shen, Zhichao Cao, Dong Zhao, JeongGil Ko, Kiran Somasundaram, Shrikanth Narayanan, Salman Avestimehr, Mi Zhang
Under Review
[Arxiv] [Github]
TL; DR: Unified framework for benchmarking federated learning in AIoT, providing diverse datasets, tasks and analyses.

[9] GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
Tuo Zhang^*, Tiantian Feng*, Samiul Alam, Dimitrios Dimitriadis, Mi Zhang, Shrikanth Narayanan, Salman Avestimehr (* means co-1st authors)
Under Review
[Arxiv]
TL; DR: A novel Federated Learning format that leverages generative pre-trained models on the server side.
[8] FedMultimodal: A Benchmark for Multimodal Federated Learning
Tiantian Feng, Digbalay Bose, Tuo Zhang, Rajat Hebbar, Anil Ramakrishna, Rahul Gupta, Mi Zhang, Salman Avestimehr, Shrikanth Narayanan
KDD 2023 – 29^th ACM SIGKDD Conference
[Arxiv] [Github]
TL; DR: A benchmark for multimodal Federated Learning.
[7] TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
Tuo Zhang, Lei Gao, Sunwoo Lee, Mi Zhang, Salman Avestimehr
CVPR 2023 FedVision (Oral)
[Proceeding] [Arxiv]
TL; DR: An inclusiveness asynchronous federated learning with adaptive partial training.

[6] Secure Federated Learning against Model Poisoning Attacks via Client Filtering
Duygu Nur Yaldiz^*, Tuo Zhang^*, Salman Avestimehr (* means co-1st authors)
ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning (BANDS)
[Arxiv] [Github]
TL; DR: A cosine-similarity-based attacker detection algorithm against model poisoning attacks under FL framework.

[5] FedAudio: A Federated Learning Benchmark for Audio Tasks
Tuo Zhang^*, Tiantian Feng*, Samiul Alam, Sunwoo Lee, Mi Zhang, Shrikanth S. Narayanan, Salman Avestimehr (* means co-1st authors)
ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
[Proceeding] [Arxiv] [Github]
TL; DR: The first benchmark paper for FL plus audio with both clean and noisy data.

[4] Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, Salman Avestimehr
IEEE Internet of Things Magazine (IEEE IoTM), March 2022
[Proceeding] [Arxiv]
TL; DR: A very good introduction paper for federated learning

[3] Layer-wise Adaptive Model Aggregation for Scalable Federated Learning
Sunwoo Lee, Tuo Zhang, Salman Avestimehr
AAAI 2023 – Thirty-Seventh AAAI Conference on Artificial Intelligence
[Proceeding] [Arxiv]
TL; DR: Layer-wise adaptive model aggregation scheme for communication-efficient Federated Learning.

[2] Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
Tuo Zhang*, Chaoyang He*, Tianhao Ma, Lei Gao, Mark Ma, Salman Avestimehr (* means co-1st authors)
ACM Embedded Networked Sensor Systems SenSys 2021 (AIChallengeIoT)
[Proceeding] [Arxiv] [GitHub]
TL; DR: IoT x Federated Learning

[1] Mobility-Aware Smart Charging of Electric Bus Fleets
A Moradipari, N Tucker, T Zhang, G Cezar, M Alizadeh
2020 IEEE Power & Energy Society General Meeting (PESGM)
[Proceeding] [Arxiv]