FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
Authors: Liping Yi, Han Yu, Zhuan Shi, Gang Wang, Xiaoguang Liu, Lizhen Cui, Xiaoxiao Li
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments present that Fed SSA achieves up to 3.62% higher accuracy, 15.54 times higher communication efficiency, and 15.52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines. |
| Researcher Affiliation | Academia | Liping Yi1,2, Han Yu2, Zhuan Shi3, Gang Wang1, , Xiaoguang Liu1, Lizhen Cui4, , Xiaoxiao Li5 1College of Computer Science, TMCC, Sys Net, DISSec, GTIISC, Nankai University, Tianjin, China 2College of Computing and Data Science, Nanyang Technological University, Singapore 3Artificial Intelligence Laboratory, Ecole Polytechnique F ed erale de Lausanne (EPFL), Switzerland 4School of Software, Shandong University, Jinan, China 5Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada |
| Pseudocode | Yes | Fed SSA is detailed in Algorithm 1 (Appendix A 1). |
| Open Source Code | Yes | https://github.com/LipingYi/FedSSA |
| Open Datasets | Yes | We evaluate Fed SSA and baselines on two image classification datasets: CIFAR-10 and CIFAR-100 3 [Krizhevsky, 2009]. |
| Dataset Splits | Yes | Then, each client s local data are further divided into the training set, the evaluation set, and the testing set following the ratio of 8:1:1. |
| Hardware Specification | Yes | baselines with Pytorch on four NVIDIA Ge Force RTX 3090 GPUs with 24G memory. |
| Software Dependencies | No | The paper mentions "Pytorch" but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | The epochs of local training E 2 {1, 10} and the batch size of local training B 2 {64, 128, 256, 512}. The optimizer for local training is SGD with learning rate = 0.01. We also tune special hyperparameters for the baselines and report the optimal results. We also adjust the hyperparameters µ0 and Tstable to achieve the best-performance Fed SSA. To compare Fed SSA with the baselines fairly, we set the total number of communication rounds T 2 {100, 500} to ensure all algorithms converge. |