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..
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 | Venue PDF | 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. |