Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework
Authors: Shuai Wang, Yanqing Xu, Zhiguo Wang, Tsung-Hui Chang, Tony Q. S. Quek, Defeng Sun
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments based on (semi-)supervised image classification tasks demonstrate superiority of Fed VRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling. |
| Researcher Affiliation | Academia | 1 Information Systems Technology and Design, Singapore University of Technology and Design, 487372 Singapore 2 School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China 3 College of Mathematics, Sichuan University, Chengdu, Sichuan 610064, China 4 Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong |
| Pseudocode | Yes | Algorithm 1: Proposed Fed VRA algorithm |
| Open Source Code | No | No explicit statement providing access to open-source code (e.g., a specific repository link or a clear statement about code availability in supplementary materials) was found. |
| Open Datasets | Yes | The CIFAR-10 (Krizhevsky and Hinton 2009) and MNIST (Le Cun, Cortes, and Burges 2010) datasets are considered for evaluation. |
| Dataset Splits | No | The paper describes the distribution of training samples to clients (N=100) and the method for Non-IID data partitioning, and states that testing samples are used for test accuracy. However, it does not explicitly mention a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) that are needed to replicate the experiment. |
| Experiment Setup | Yes | The learning rate η is set to be 0.01 for all algorithms. The mini-batch size S is set to be 50. In each round, we uniformly sample 10 clients (|Ar| = 10), and randomly choose the number of local epochs from [1, 5] if HLU is considered, and otherwise set it to 2 for each client. Other algorithm specific parameters are tuned individually. Fed Prox uses µ = 0.1 and SCAFFOLD takes ηg = 1. Both the parameter α of Fed Dyn and γi of Fed VRA are 0.1 by default. All algorithms stop when 500 rounds are achieved. |