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..
Coordinating Momenta for Cross-Silo Federated Learning
Authors: An Xu, Heng Huang8735-8743
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive deep FL experimental results verify that our new approach has a better training performance than the Fed Avg and existing standard momentum SGD variants. |
| Researcher Affiliation | Academia | Electrical and Computer Engineering Department, University of Pittsburgh, PA, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: FL with double momenta. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code release or repository links for the described methodology. |
| Open Datasets | Yes | We train VGG-16 (Simonyan and Zisserman 2014) and Res Net-56 (He et al. 2016) models on CIFAR-10/1001 (Krizhevsky 2009), and Res Net-20 on SVHN2 image classification tasks. 1https://www.cs.toronto.edu/ kriz/cifar.html 2http://ufldl.stanford.edu/housenumbers/ |
| Dataset Splits | Yes | We follow (Karimireddy et al. 2020b) to simulate the non-i.i.d. data distribution. Specifically, fraction s of the data are randomly selected and allocated to clients, while the remaining fraction 1 s are allocated by sorting according to the label. The data similarity is hence s. We run experiments with data similarity s in {5%, 10%, 20%}. By default, the data similarity is set to 10% and the number of clients (GPUs) K = 16 following (Wang et al. 2020). and We use local epoch E instead of local training steps P in experiments. E = 1 is identical to one pass training of local data. We test local epoch E {0.5, 1, 2} and E = 1 by default. |
| Hardware Specification | Yes | All experiments are implemented using Py Torch (Paszke et al. 2019) and run on a cluster where each node is equipped with 4 Tesla P40 GPUs and 64 Intel(R) Xeon(R) CPU E5-2683 v4 cores @ 2.10GHz. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2019)' but does not specify a version number for PyTorch or other software dependencies. |
| Experiment Setup | Yes | We perform careful hyper-parameters tuning for all methods. The local momentum constant µl is selected from {0.9, 0.8, 0.6, 0.4, 0.2}. We select the server momentum constant µs from {0.9, 0.6, 0.3}. The base learning rate is selected from {..., 4 10 1, 2 10 1, 1 10 1, 5 10 2, 1 10 2, 5 10 3, ...}. The server learning rate α is selected from {0.2, 0.4, 0.6, 0.8, 0.9, 1.0}. The momentum fusion constant β is selected from {0.2, 0.4, 0.6, 0.8, 0.9, 1.0}. |