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..
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
Authors: Xiang Liu, Liangxi Liu, Feiyang Ye, Yunheng Shen, Xia Li, Linshan Jiang, Jialin Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that Fed LPA significantly improves learning performance over state-of-the-art methods across several metrics. |
| Researcher Affiliation | Academia | Xiang Liu1, , Liangxi Liu2, , Feiyang Ye3, Yunheng Shen4, Xia Li5, Linshan Jiang1, , Jialin Li1, 1National University of Singapore, 2Northeastern University, 3University of Technology Sydney, 4Tsinghua University, 5ETH Zurich |
| Pseudocode | Yes | Algorithm 1 Fed LPA Global Aggregation |
| Open Source Code | Yes | Our Fed LPA is available in https://github.com/lebronlambert/Fed LPA_Neur IPS2024. |
| Open Datasets | Yes | We conduct experiments on MNIST [63], Fashion-MNIST [64], CIFAR-10 [65], and SVHN [66] datasets. |
| Dataset Splits | Yes | We use the data partitioning methods for non-IID settings of the benchmark 1 to simulate different label skews. Specifically, we try two different kinds of partition: 1) #C = k: each client only has data from k classes. ... 2) pk Dir(β): for each class, we sample from Dirichlet distribution pk and distribute pk,j portion of class k samples to client j. |
| Hardware Specification | Yes | We conduct experiments on CIFAR-10 on a single 2080Ti GPU to estimate the overall communication and computation overhead. |
| Software Dependencies | No | The paper mentions 'Pytorch' in the context of floating point precision ('The default floating point precision is 32 bits in Pytorch.'), but does not specify a version number or list other software dependencies with version numbers. |
| Experiment Setup | Yes | We set the batch size to 64, the learning rate to 0.001, and the λ = 0.001 for Fed LPA. By default, we set 10 clients and run 200 local epochs for each client. |