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 [1].
Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning
Authors: Haibo Yang, Peiwen Qiu, Jia Liu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to theoretical analysis, we also conduct extensive numerical experiments to study the fattailed phenomenon in FL systems and verify the efficacy of our proposed FAT-Clipping algorithms for FL systems with fat-tailed noise. |
| Researcher Affiliation | Academia | Haibo Yang Dept. of ECE The Ohio State University Columbus, OH 43210 EMAIL Peiwen Qiu Dept. of ECE The Ohio State University Columbus, OH 43210 EMAIL Jia Liu Dept. of ECE The Ohio State University Columbus, OH 43210 EMAIL |
| Pseudocode | Yes | Algorithm 1 Generalized Fed Avg Algorithm (GFed Avg). Algorithm 2 The FAT-Clipping-PR Algorithm. Algorithm 3 The FAT-Clipping-PI Algorithm. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | In this section, we conduct numerical experiments to verify the theoretical findings in Section 4 using 1) a synthetic function, 2) a convolutional neural network (CNN) with two convolutional layers on CIFAR-10 dataset [43], and 3) RNN on Shakespeare dataset. |
| Dataset Splits | No | The paper describes data distribution across clients (i.i.d. vs. non-i.i.d.) and mentions using standard procedures, but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts) in the main text. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware used for its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software or libraries used in the experiments. |
| Experiment Setup | No | The paper mentions general experimental setup aspects like the number of clients and data heterogeneity parameter 'p', but it does not provide specific hyperparameter values such as learning rates, batch sizes, or optimizer settings used in the numerical experiments. |