Knowledge-Aware Parameter Coaching for Personalized Federated Learning
Authors: Mingjian Zhi, Yuanguo Bi, Wenchao Xu, Haozhao Wang, Tianao Xiang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |