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

Los Angeles, USA

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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. Tuo is also an almuni of Nankai High School, Tianjin(天津南开中学). His research now focuses on distributed/federated machine learning algorithms, systems, and applications.

Publications

• [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)
  [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]
  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, Chaoyang He, Salman Avestimehr
  AAAI 2023 – Thirty-Seventh AAAI Conference on Artificial Intelligence
  [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]

Recent News

• 05/17/2023: Our paper, FedMultimodal: A Benchmark for Multimodal Federated Learning, has been accepted to KDD 2023. We will release the camera-ready paper soon!
• 03/31/2023: Our paper, TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training, has been accepted to CVPR 2023 (FedVision). See you in Vancouver! 🏙️
• 03/04/2023: Our paper, Secure Federated Learning against Model Poisoning Attacks via Client Filtering, has been accepted to ICLR BANDS 2023.
• 02/15/2023: Our paper, FedAudio: A Federated Learning Benchmark for Audio Tasks, has been accepted to ICASSP 2023. See you in Greece! 🇬🇷
• 11/19/2022: Our paper, Layer-wise adaptive model aggregation for scalable federated learning, has been accepted to AAAI 2023. See you in DC! 🏛️
• 10/30/2022: We have released the FedAudio, the first federated learning benchmark dataset for audio tasks!
• 03/04/2022: Our FL + IoT Survey paper has been accepted to IEEE Internet of Things Magazine (IEEE IoTM), March 2022 Special Issue: An End-to-end Machine Learning Perspective on Industrial IoT
• 10/20/2021: Our FedIoT paper has been admitted to accepted to ACM Embedded Networked Sensor Systems SenSys 2021 (AIChallengeIoT)
• 08/01/2020: Joined USC as a Ph.D. student in summer
• 06/15/2020: Graduated from UCSB as a B.S. with High Honor

Professional Activities

• Journal Reviews
  • IEEE Journal on Selected Areas in Communications (IEEE J-SAC)
    (1 Review)
  • IEEE Transactions on Mobile Computing (1 Review)
  • Journal of Software: Practice and Experience (1 Review)

• Conference Reviews
  • ICML 2022 (2 Review)
  • NeurIPS 2022 (5 Review)
  • AAAI 2023 (6 Review)
  • AISTATS 2023 (4 Review)
  • ICML 2023 (6 Review)

• Book Series Proposal Reviews
  • Springer Nature Computer Science