Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization
Authors: Jun Chen, Hong Chen, Bin Gu, Hao Deng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper aims to establish systematic theoretical assessments of Fed ZO by developing the analysis technique of on-average model stability. We establish the first generalization error bound of Fed ZO under the Lipschitz continuity and smoothness conditions. Then, refined generalization and optimization bounds are provided by replacing bounded gradient with heavy-tailed gradient noise and utilizing the second-order Taylor expansion for gradient approximation. With the help of a new error decomposition strategy, our theoretical analysis is also extended to the asynchronous case. For Fed ZO, our fine-grained analysis fills the theoretical gap on the generalization guarantees and polishes the convergence characterization of the computing algorithm. |
| Researcher Affiliation | Academia | 1College of Informatics, Huazhong Agricultural University, China 2Engineering Research Center of Intelligent Technology for Agriculture, China 3School of Artificial Intelligence, Jilin University, China 4Mohamed bin Zayed University of Artificial Intelligence 5Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, China 6Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, China |
| Pseudocode | Yes | Algorithm 1 Synchronous Fed ZO |
| Open Source Code | No | The paper does not provide any statement about making its source code available or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with empirical data. Therefore, it does not mention or provide access information for a publicly available or open dataset for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with empirical data. Therefore, it does not provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments run on specific hardware. Thus, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical analysis and proofs, not empirical experiments. Therefore, it does not provide details about an experimental setup, such as hyperparameters or system-level training settings. |