Distributed Event-Based Learning via ADMM
Authors: Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The article concludes by presenting numerical results from distributed learning tasks on the MNIST and CIFAR-10 datasets. The experiments underline communication savings of 35% or more due to the event-based communication strategy, show resilience towards heterogeneous data-distributions, and highlight that our approach outperforms common baselines such as Fed Avg, Fed Prox, SCAFFOLD and Fed ADMM. |
| Researcher Affiliation | Academia | 1 Learning and Dynamical Systems Group, Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany 2 Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany. Correspondence to: Guener Dilsad ER <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Event-Based Distributed Learning with Over Relaxed ADMM Algorithm 2 Event-Based Distributed Optimization with Over-Relaxed ADMM Algorithm 3 Event-Based Distributed Optimization with Over-Relaxed ADMM |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | The article concludes by presenting numerical results from distributed learning tasks on the MNIST and CIFAR-10 datasets. ... We start by evaluating the performance of Alg. 1 on MNIST (Deng, 2012) and CIFAR-10 (Krizhevsky, 2009). |
| Dataset Splits | Yes | Our setup included N =10 agents for MNIST, each storing data for a single digit, resulting in the most extreme non-i.i.d. distribution of data among agents. For a CIFAR-10 classifier, the data are distributed among N =100 agents according to a Dirichlet distribution, i.e., we sample pa Dir N(β), where N is the number of agents and β =0.5. We then assign a pa,j proportion of the training data of class a to agent j. |
| Hardware Specification | No | The paper does not provide specific hardware details (GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Table 3: The table summarizes the hyperparameters used for distributed training of MNIST classifier (Fig. 8, Tab. 1) Table 4: The table summarizes the hyperparameters used for the distributed training of CIFAR-10 classifier (Fig. 8, Tab. 1) Table 5: The table summarizes the hyperparameters used for the distributed linear regression and LASSO experiments (Fig. 9). Table 6: The table summarizes the hyperparameters used for the distributed LASSO experiment against communication drops (Fig. 10). Table 7: The table summarizes the hyperparameters used for the distributed training of MNIST classifier over a graph (Fig. 11). Table 8: The table summarizes the hyperparameters used for the distributed linear regression experiment over a graph (Fig. 12). |