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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Distributed Machine Learning through Heterogeneous Edge Systems
Authors: Hanpeng Hu, Dan Wang, Chuan Wu7179-7186
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our testbed implementation and experiments show that ADSP outperforms existing parameter synchronization models significantly in terms of ML model convergence time, scalability and adaptability to large heterogeneity. 5 Performance Evaluation We implement ADSP as a ready-to-use Python library based on Tensor Flow (Abadi et al. 2016), and evaluate its performance with testbed experiments. |
| Researcher Affiliation | Academia | Hanpeng Hu,1 Dan Wang,2 Chuan Wu1 1The University of Hong Kong, 2The Hong Kong Polytechnic University |
| Pseudocode | Yes | Algorithm 1 Commit Rate Adjustment at the Scheduler |
| Open Source Code | No | The paper states 'We implement ADSP as a ready-to-use Python library based on Tensor Flow', but does not provide any link or explicit statement about making this library open-source or publicly available. |
| Open Datasets | Yes | (i) image classification on Cifar-10 (Krizhevsky and Hinton 2010) using a CNN model from the Tensor Flow tutorial (Tensorflow 2019) |
| Dataset Splits | No | The paper mentions using the Cifar-10 dataset and training with mini-batches, but it does not provide specific details on how the dataset was split into training, validation, and test sets (e.g., percentages or sample counts). |
| Hardware Specification | Yes | Testbed. We emulate heterogeneous edge systems following the distribution of hardware configurations of edge devices in a survey (Jkielty 2019), using 19 Amazon EC2 instances (Wang and Ng 2010): 7 t2.large instances, 5 t2.xlarge instances, 4 t2.2xlarge instances and 2 t3.xlarge instances as workers, and 1 t3.2xlarge instance as the PS. |
| Software Dependencies | No | The paper states it is 'based on Tensor Flow', but does not provide a specific version number for Tensor Flow or any other software dependencies with their versions. |
| Experiment Setup | Yes | Default Settings. By default, each mini-batch in our model training includes 128 examples. The check period of ADSP is 60 seconds, and each epoch is 20 minutes long. The global learning rate is 1/M (which we find works well through experiments). The local learning rate is initialized to 0.1 and decays exponentially over time. |