Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization
Authors: Yuyang Qiu, Uday Shanbhag, Farzad Yousefian
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,762 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,936 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the theory on nonsmooth and hierarchical ML problems. We present three sets of experiments to validate the performance of the proposed algorithms. In Section 5.1, we implement Algorithm 1 on Re LU neural networks (NNs) and compare it with some recent FL methods. In Sections 5.2 and 5.3 we implement Algorithm 2 on federated hyperparameter learning and a minimax formulation in FL. Throughout, we use the MNIST dataset. Additional experiments on a higher dimensional dataset (i.e., Cifar-10) are presented in supplementary material. |
| Researcher Affiliation | Academia | Yuyang Qiu Dept. of Industrial and Systems Engg. Rutgers University EMAIL Uday V. Shanbhag Dept. of Industrial and Manufacturing Engg. Pennsylvania State University EMAIL Farzad Yousefian Dept. of Industrial and Systems Engg. Rutgers University EMAIL |
| Pseudocode | Yes | Algorithm 1 Randomized Zeroth-Order Locally-Projected Federated Averaging (Fed RZOnn) Algorithm 2 Randomized Implicit Zeroth-Order Federated Averaging (Fed RZObl) Algorithm 3 Fed Avg (x, r, y0,r, m, γ, H, T R) for lower level |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | Yes | We present three sets of experiments to validate the performance of the proposed algorithms. In Section 5.1, we implement Algorithm 1 on Re LU neural networks (NNs) and compare it with some recent FL methods. In Sections 5.2 and 5.3 we implement Algorithm 2 on federated hyperparameter learning and a minimax formulation in FL. Throughout, we use the MNIST dataset. Additional experiments on a higher dimensional dataset (i.e., Cifar-10) are presented in supplementary material. |
| Dataset Splits | No | The paper mentions distributing the training dataset among clients and using MNIST and CIFAR-10, but it does not specify explicit training, validation, and test splits (e.g., percentages, sample counts, or references to standard splits for these datasets) that would allow for reproduction of data partitioning. |
| Hardware Specification | No | The paper describes the experimental setup and training details, but it does not provide any specific information regarding the hardware used (e.g., CPU, GPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions using ReLU neural networks and comparing with other FL methods like Fed Avg, but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | Setup. We distribute the training dataset among m := 5 clients and implement Fed RZOnn for the FL training with N1 := 4 neurons under three different settings for the smoothing parameter, η {0.1, 0.01, 0.001}, γ := 10 5, and λ := 0.01. We study the performance of the method under different number of local steps with H {1, 5, 10, 20}. |