Weijie (Vinny) Liu

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Master student in SYSU

Education

Academic Experience

Smart Mobile Computing Lab, Sun Yat-sen University (Sept. 2021 - Present)

Academic Service

Skills and Interests

Projects

Leverage the Interplay of mini-batch size and aggregation frequency in Federated Learning

Publication

We propose a novel algorithm, named CoOptFL, by quantifying the interplay of mini-batch size and aggregation frequency to navigate the trade-offs among model convergence, completion time, and resource cost in federated learning. Based on which, we propose an online adaptive optimization algorithm AdaCoOpt by integrating the online estimates of the fluctuating edge network characteristics into CoOptFL. Experiments show a 37.6% reduction in the total training cost under a cost-sensitive scenario, and can achieve a 2.7%–7.9% higher final test accuracy than baselines

AdaCoOpt

Publications

  1. W. Liu, X. Zhang, J. Duan, C. Joe-Wong, Z. Zhou and X. Chen, DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasets IEEE Transactions on Mobile Computing.
  2. W. Liu, X. Zhang, J. Duan, C. Joe-Wong, Z. Zhou and X. Chen, AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning, accepted to IEEE/ACM IWQoS 2023 (Best Paper Finalist).
  3. W. Liu, X. Zhang, J. Duan, C. Joe-Wong, Z. Zhou and X. Chen, Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency, accepted to IEEE INFOCOM 2023 FOGML WORKSHOP.

Honors and Awards