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..
CoDeC: Communication-Efficient Decentralized Continual Learning
Authors: Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha, Kaushik Roy
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical evidence by performing experiments over various standard image-classification datasets, networks, graph sizes, and topologies. We also extend our analysis by designing and evaluating a decentralized continual learning benchmark Med MNIST-5 using biomedical image-classification datasets from Med MNIST-v2 (Yang et al., 2021). This imitates a practical real-world application where multiple healthcare organizations aim to learn a global generalized model without sharing the locally accessible patients data. |
| Researcher Affiliation | Academia | Sakshi Choudhary EMAIL Department of Electrical and Computer Engineering Purdue University; Sai Aparna Aketi EMAIL Department of Electrical and Computer Engineering Purdue University; Gobinda Saha EMAIL Department of Electrical and Computer Engineering Purdue University; Kaushik Roy EMAIL Department of Electrical and Computer Engineering Purdue University |
| Pseudocode | Yes | Algorithm 1 Communication-Efficient Decentralized Continual Learning (Co De C); Algorithm 2 Decentralized Elastic Weight Consolidation (D-EWC); Algorithm 3 Decentralized Synaptic Intelligence (D-SI) |
| Open Source Code | Yes | 1The PyTorch implementation can be found at https://github.com/Sakshi09Ch/Co De C |
| Open Datasets | Yes | We evaluate Co De C on three well-known continual learning benchmark datasets: 10-Split CIFAR-100 (Krizhevsky, 2009), 20-Split Mini Image Net (Vinyals et al., 2016) and a sequence of 5-Datasets (Ebrahimi et al., 2020). 10-Split CIFAR-100 is constructed by splitting CIFAR-100 into 10 tasks, where each task comprises of 10 classes. The sequence of 5-Datasets includes CIFAR-10, MNIST, SVHN (Netzer et al., 2011), not MNIST (Bulatov, 2011) and Fashion MNIST (Xiao et al., 2017). We design Med MNIST-5, a biomedical decentralized continual learning benchmark based on the datasets in Med MNIST-v2 (Yang et al., 2021). |
| Dataset Splits | Yes | For each task, the data distribution is IID across the agents. [...] For instance, for a graph size of 4 agents, each agent has 5000/4 = 1250 training samples for a particular task in Split CIFAR-100. [...] Table 8: Dataset Statistics for Split CIFAR-100 and Split-mini Image Net; Split CIFAR-100: # Training samples/tasks 5,000, # Test samples/tasks 1,000; Split mini Image Net: # Training samples/tasks 2,500, # Test samples/tasks 500 |
| Hardware Specification | Yes | We perform our experiments on a single machine with 4 NVIDIA GeForce GTX 1080 Ti GPUs. |
| Software Dependencies | No | The PyTorch implementation can be found at https://github.com/Sakshi09Ch/Co De C. The paper mentions PyTorch but does not provide a specific version number. |
| Experiment Setup | Yes | All our experiments were run for three randomly chosen seeds. We decay the learning rate by a factor of 10 after 50% and 75% of the training, unless mentioned otherwise. For Split CIFAR-100, we use a mini-batch size of 22 per agent, and we run all our experiments for a total of 100 epochs for each task. For Split Mini Image Net, we use a mini-batch size of 10 per agent and 10 epochs for each task. For 5-Datasets and Med MNIST-5, we use a mini-batch size of 32 per agent, and 50 epochs for each task. We list additional hyperparameters in Table 10. |