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..
Formal Logic Enabled Personalized Federated Learning through Property Inference
Authors: Ziyan An, Taylor T. Johnson, Meiyi Ma
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method on two tasks: a real-world traffic volume prediction task consisting of sensory data from fifteen states and a smart city multi-task prediction utilizing synthetic data. The evaluation results exhibit clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models. |
| Researcher Affiliation | Academia | Ziyan An, Taylor T. Johnson, Meiyi Ma Department of Computer Science, Vanderbilt University, Nashville, TN, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: CLUSTER ID: Cluster identity mapping; Algorithm 2: Fed STL: Client federation and update |
| Open Source Code | Yes | Code implementation is available at https://github.com/AICPSLab/Fed STL.git. |
| Open Datasets | Yes | We obtain a publicly available dataset from the Federal Highway Administration (FHWA 2016) and preprocess hourly traffic volume from 15 states. ... we create a simulated dataset using SUMO (Simulation of Urban MObility) (Krajzewicz et al. 2002), a large-scale open-source road traffic simulator. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or k-fold cross-validation setup). |
| Hardware Specification | Yes | The experiments were conducted on a machine equipped with an Intel Core i9-10850K CPU and an NVIDIA GeForce RTX 3070 GPU. |
| Software Dependencies | No | The paper mentions 'The operating system used was Ubuntu 18.04.' but does not list specific versions for other key software components like machine learning frameworks (e.g., PyTorch, TensorFlow) or libraries. |
| Experiment Setup | Yes | During each round of FL communication, we randomly select 10% of the client devices to participate. For all the conducted experiments and algorithms, we use SGD with consistent learning rates and a batch size of 64. ... We set the number of local epochs to 10 for Fed Avg, Fed Prox, Fed Rep (with 8 head epochs), Ditto, and IFCA. Additionally, for Fed STL, we employ 6 local epochs and 4 cluster training epochs. |