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..
Knowledge-Aware Parameter Coaching for Personalized Federated Learning
Authors: Mingjian Zhi, Yuanguo Bi, Wenchao Xu, Haozhao Wang, Tianao Xiang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted over various datasets, which show that the proposed method can achieve better performance compared with the state-of-the-art baselines in terms of accuracy and convergence speed. |
| Researcher Affiliation | Academia | 1Northeastern University, China 2The Hong Kong Polytechnic University, Hong Kong, China 3Huazhong University of Science and Technology, China |
| Pseudocode | Yes | Algorithm 1: Parameter Coaching Process in the Server, Algorithm 2: Parameter Coaching Process in Client i |
| Open Source Code | No | The paper does not provide an explicit statement or link for the availability of its source code. |
| Open Datasets | Yes | Four public benchmark datasets are used to evaluate the proposed method, MNIST, FMNIST, CIFAR10 and CIFAR100. |
| Dataset Splits | No | The paper mentions training and test data but does not explicitly state a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running the experiments. |
| Software Dependencies | No | All the experiments are repeated over 3 runs in Pytorch. (No version specified) |
| Experiment Setup | Yes | The model is trained by K = 50 rounds on MNIST/FMNIST, K = 100 rounds on CIFAR10, and K = 200 rounds on CIFAR100. The local epochs for W and R are set to 5 and 1 for all cases. In addition, cross-entropy loss and stochastic gradient descent method are adopted to update the client parameters and relation cube, and the learning rates for W and R are both set to 0.01. |