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..
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Authors: Zexi Li, Tao Lin, Xinyi Shang, Chao Wu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify that our method can improve the generalization of the global model by a large margin on different datasets and models. |
| Researcher Affiliation | Academia | 1Zhejiang University, China. Zexi Li <EMAIL>. 2Research Center for Industries of the Future, Westlake University, China. 3Xiamen University, China. Xinyi Shang <EMAIL>. Work was done during Xinyi s visit to Westlake University. Correspondence to: Chao Wu <EMAIL>, Tao Lin <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 FEDLAW: Federated Learning with Learnable Aggregation Weights |
| Open Source Code | No | Based on the above insights, we propose an effective method for Federated Learning with Learnable Aggregation Weights, named as FEDLAW ( source code). The phrase "( source code)" is present in the abstract, but it is not an active link or a clear URL to the code repository, indicating that concrete access is not provided in the paper itself. |
| Open Datasets | Yes | For concision, in section 4 and section 5, if not mentioned otherwise, we all use CIFAR-10 as the dataset and Simple CNN as the model. ... We conduct experiments to verify the effectiveness of FEDLAW. ... Dataset Fashion MNIST CIFAR-10 CIFAR-100. These are well-known public datasets commonly used in ML research. |
| Dataset Splits | No | The paper mentions using a "proxy dataset" for learning aggregation weights, and training on "trainset" and evaluating on "testset", but it does not specify a separate validation split for hyperparameter tuning or model selection during training. |
| Hardware Specification | Yes | We use 4 Quadro RTX 8000 GPUs for computation. |
| Software Dependencies | Yes | We conduct experiments under Python 3.8.5 and Pytorch 1.12.0. |
| Experiment Setup | Yes | We set the initial learning rates (LR) as 0.08 in CIFAR-10 and Fashion MNIST and set LR as 0.01 in CIFAR-100. We set a decaying LR scheduler in all experiments; that is, in each round, the local LR is 0.99*(LR of the last round). Local weight decay. We adopt local weight decay in all experiments. For CIFAR-10 and Fashion MNIST, we set the weight decay factor as 5e-4, and for CIFAR-100, we set it as 5e-5. Optimizer. We set SGD optimizer as the clients local solver and set momentum as 0.9. For the server-side optimizer (FEDDF, FEDBE, SERVER-FT, and FEDLAW), we use Adam optimizer and betas=(0.5, 0.999). |