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..
Stochastic Controlled Averaging for Federated Learning with Communication Compression
Authors: Xinmeng Huang, Ping Li, Xiaoyun Li
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that SCALLION and SCAFCOM outperform recent compressed FL methods under the same communication budget. We conduct experiments to illustrate the effectiveness of our proposed methods. |
| Researcher Affiliation | Collaboration | The work is conducted at Linked In Bellevue, 98004 WA, USA. Xinmeng Huang is a Ph.D. student in the Graduate Group of Applied Mathematics and Computational Science at the University of Pennsylvania. |
| Pseudocode | Yes | Algorithm 1 SCALLION: SCAFFOLD with single compressed uplink communication. Algorithm 2 SCAFCOM: SCAFFOLD with momentum-enhanced compression. |
| Open Source Code | No | No explicit statement or link indicating the release of open-source code for the described methodology was found. |
| Open Datasets | Yes | We test our algorithms on two standard FL datasets: MNIST dataset (Le Cun, 1998) and Fashion MNIST dataset (Xiao et al., 2017). |
| Dataset Splits | No | The training data are distributed across N = 200 clients, in a highly heterogeneous setting following (Li & Li, 2023). The training data samples are split into 400 shards each containing samples from only one class. Then, each client is randomly assigned two shards of data. |
| Hardware Specification | No | No specific hardware specifications (e.g., GPU models, CPU types, memory) for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., programming language, libraries, frameworks) were mentioned in the paper. |
| Experiment Setup | Yes | In each round of client-server interaction, we uniformly randomly pick S = 20 clients to participate in FL training, i.e., the partial participation rate is 10%. Each participating client performs K = 10 local training steps using the local data, with a mini-batch size 32... We tune the combination of the global learning rate ηg and the local learning rate ηl over the 2D grid {0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10}2. |