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..
Differentially Private and Communication Efficient Collaborative Learning
Authors: Jiahao Ding, Guannan Liang, Jinbo Bi, Miao Pan7219-7227
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed methods are evaluated in extensive experiments on real-world datasets and the empirical results validate our theoretical findings. |
| Researcher Affiliation | Academia | 1University of Houston 2University of Connecticut EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Q-DPSGD-1 run by agent i, Algorithm 2 Q-DPSGD-2 run by agent i |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | We conduct the experiments over two benchmark datasets: MNIST and CIFAR-10. |
| Dataset Splits | No | The paper mentions training data ("randomly sample 10,000 records for training") and implicitly test data (through performance comparison figures), but does not explicitly describe a validation set or a three-way split for train/validation/test. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using Python for implementation (implied by typical ML papers) but does not provide specific version numbers for Python, libraries, or other software dependencies. |
| Experiment Setup | Yes | In the experiments, we set the step sizes (α, ε) = (0.3/T 1/6, 11/T 1/2) for Q-DPSGD-1 and Q-DPSGD-2, and α = 0.2 for DSGD and SDM. Moreover, we also set θ = 0.6 as stated in (Zhang et al. 2020) for SDM. To control the sensitivity of the gradient, we adopt gradient clipping threshold technique, ℓ(xi,t; θ) = ℓ(xi,t; θ)/ max (1, ℓ(xi,t; θ) /K). Here, we set K = 0.5 for Q-DPSGD-1 and Q-DPSGD-2 and SDM. In each simulation, we randomly sample 10,000 records for training and divide them into n parties, and thus each party consists of 10000/n data samples (i.e., m = 10000/n). In all experiments, we set δ = 10 5. We also set the processing speed of each machine follows a uniform distribution given as V Uniform(10, 90), and then choose the deadline Td = B/E[V ], where B is the expected batch size used in each machine. We consider a low precision quantizer in (5) with various quantization levels s, and we denote Tc as the communication time of a p-dimension vector without quantization (16 bits). |