Revisiting Differentially Private Algorithms for Decentralized Online Learning
Authors: Xiaoyu Wang, Wenhao Yang, Chang Yao, Mingli Song, Yuanyu Wan
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct simulation experiments with convex functions to verify the performance of our algorithms. |
| Researcher Affiliation | Academia | 1School of Software Technology, Zhejiang University, Ningbo, China 2National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 3State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China. Correspondence to: Yuanyu Wan <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 PD-FTGL; Algorithm 2 Private Sum (Smith & Thakurta, 2013); Algorithm 3 PD-OCG; Algorithm 4 CG (Frank & Wolfe, 1956; Jaggi, 2013) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of source code for the described methodology. |
| Open Datasets | Yes | Moreover, we use two publicly available datasets letter and poker from the LIBSVM repository (Chang & Lin, 2011), and their details are summarized in Table 2. |
| Dataset Splits | Yes | For letter, we construct a larger one including 10n copies of the original data, and evenly distribute them among the n local learners, which implies that T = 150000. For poker, we construct a larger one including n copies of the original data, and distribute them in the same way, which implies that T = 1000000. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers or specialized package versions used for the implementation of the algorithms or experiments. |
| Experiment Setup | Yes | Specifically, let p and q denote the number of features and classes, respectively. The decision set of is defined as K = {X Rq p | X τ}, where X denotes the trace norm of X and τ = 10. [...] By default, we set n = 9 and use the complete graph... Moreover, following previous studies on DP (Abadi et al., 2016; Kairouz et al., 2021), we substitute the original gradient ft,i(Xi(t)) by a clipping gradient defined as gclip(t, i) = ft,i(Xi(t))/ max{1, ft,i(Xi(t))F } where the constant C is tuned from the set {0.01, 0.1, 1, 10} to obtain the best performance... Additionally, for the parameter h of our algorithms, we multiply the theoretical value by a constant tuned from the set {0.0001, 0.0005, 0.001, 0.005, ..., 10, 50} for the best performance. Other parameters of all algorithms are set as what their corresponding theories suggest. |