Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning
Authors: Zhe Li, Seyedsina Nabavirazavi, Bicheng Ying, Sitharama Iyengar, Haibo Yang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that FAST significantly improves performance under ACP and high data heterogeneity. ... We perform extensive experiments on Fashion-MNIST [Xiao et al., 2017] and CIFAR-10 [Krizhevsky et al., 2009]... |
| Researcher Affiliation | Collaboration | 1Rochester Institute of Technology 2Florida International University 3Google Inc. EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Federated Average with Snapshot (FAST) Algorithm 2 Adaptive q in FAST |
| Open Source Code | No | The paper does not contain any explicit statements about releasing the source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We perform extensive experiments on Fashion-MNIST [Xiao et al., 2017] and CIFAR-10 [Krizhevsky et al., 2009], considering various Non-IID degrees and utilizing the four distributions to simulate different client participation. ... We employ Fashion-MNIST [Xiao et al., 2017] and CIFAR-10 datasets [Krizhevsky et al., 2009] for image classification tasks, and we utilize the Shakespeare dataset [Caldas et al., 2018] for the next character prediction task. |
| Dataset Splits | No | The paper describes data partitioning for clients based on Non-IID degrees using Dirichlet distribution and client participation rates (10%), but it does not specify the standard training/validation/test splits of the datasets themselves (e.g., specific percentages or sample counts for Fashion-MNIST, CIFAR-10, or Shakespeare). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Fed Lab [Zeng et al., 2023]' for data partitioning but does not specify its version or any other software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Initialize: model parameter x0, learning rate ηc, local update steps K, communication rounds R, snapshot step interval I (or probability q). ... Our FL system comprises 100 clients in total for Fashion-MNIST and CIFAR-10 and 139 clients for Shakespeare. In each round, only 10% clients are chosen to participate in training. ... We conduct a series of experiments to assess the performance under different λ. |