Understanding the Stability-based Generalization of Personalized Federated Learning
Authors: Yingqi Liu, Qinglun Li, Jie Tan, Yifan Shi, Li Shen, Xiaochun Cao
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Promising experiments on CIFAR datasets also corroborate our theoretical insights. Our code can be seen in https://github.com/Yingqi Liu1999/Understanding-the Stability-based-Generalization-of-Personalized-Federated-Learning. [...] Massive experiments verify theoretical findings. Our experiments on CIFAR datasets with different models under non-convex conditions strongly support our theoretical insights. |
| Researcher Affiliation | Academia | Yingqi Liu1,2 Qinglun Li3 Jie Tan4 Yifan Shi Li Shen1,2 Xiaochun Cao1 1School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, China 2Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), China 3College of Systems Engineering, National University of Defense Technology, China 4Intelligent Game and Decision Lab, China EMAIL; EMAIL; EMAIL EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1: Local updating for PFL. Input :Local steps K, local learning rate ηu and ηv, initialize ut i,0 = ut, and vt i,0 = vt i. Output :For each client, locally update ut+1 i , vt+1 i . Algorithm 2: C-PFL and D-PFL. Input :Total communication rounds T, number of selected clients n, initial the shared and personal variables u0, v0 = {v0 i }n i=0. Output :Personal solution u T and v T = {v T i }n i=0. |
| Open Source Code | Yes | Our code can be seen in https://github.com/Yingqi Liu1999/Understanding-the Stability-based-Generalization-of-Personalized-Federated-Learning. |
| Open Datasets | Yes | We conduct the experiments on CIFAR-10 datasets (Krizhevsky et al., 2009) in the Dirichlet distribution (Non-IID α = 0.3) with Res Net-18 (He et al., 2016) and CIFAR-100 datasets in the Pathological distribution (Non-IID c = 20) with VGG-11 (Simonyan & Zisserman, 2014) for C-PFL and D-PFL. |
| Dataset Splits | No | The paper mentions 'training and testing error' but does not explicitly state the specific split percentages or sample counts for these splits. It describes a mechanism for generating neighboring datasets for stability analysis, but this is not standard train/test/validation split information. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only mentions models (ResNet-18, VGG-11) and datasets (CIFAR-10, CIFAR-100). |
| Software Dependencies | No | The paper mentions using SGD as the base local optimizer but does not specify any software libraries, frameworks, or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We keep the same experiment setting for all methods and perform 300 communication rounds. The number of client sizes is 20. The client sampling radio is 0.2 in C-PFL, while each client communicates with 4 neighbors in D-PFL accordingly. The batch size is 128 and the number of local epochs is 5. We set SGD (Robbins & Monro, 1951) as the base local optimizer with a learning rate η = 0.1. |