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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning
Authors: Wenjing Yan, Xiangyu Zhong, Xiaolu Wang, Ying-Jun Angela Zhang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive empirical evaluations on deep learning tasks using real-world datasets. The numerical results demonstrate that Ada Mas FL consistently outperforms state-of-the-art AFL methods in runtime efficiency and exhibits exceptional robustness across diverse learning rate configurations and system conditions. |
| Researcher Affiliation | Academia | 1Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR; 2Software Engineering Institute, East China Normal University, China. Correspondence to: Xiangyu Zhong <EMAIL>, Xiaolu Wang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Mas FL: Procedures at Central Server Algorithm 2 Mas FL: Procedures at Client i Algorithm 3 Ada Mas FL: Procedures at Central Server Algorithm 4 Ada Mas FL: Procedures at Client i |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate the performance of our algorithms on the image classification task using two real-world datasets: CIFAR-10 (Li et al., 2017) and FMNIST (Xiao et al., 2017). |
| Dataset Splits | No | The paper describes how data is distributed across clients (i.i.d. or non-i.i.d. with Dirichlet distribution Dir(α=0.5)) but does not explicitly state the training/test/validation splits for the CIFAR-10 or FMNIST datasets themselves. It implicitly uses test accuracy but doesn't specify the split ratios used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running the experiments. It only describes the simulation setup for asynchronous conditions. |
| Software Dependencies | No | The paper mentions 'stochastic gradient descent (SGD) as the optimization algorithm' and 'convolutional neural network (CNN)' or 'Res Net-18 architecture'. However, it does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions) that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Our experiments consider a federated learning system with N = 100 clients cooperating to train a shared model. At each global communication round, a fraction of 0.1 clients are randomly selected to participate, resulting in S = 10 active clients per round. Each selected client trains locally for 2 epochs with a batch size of 100 using stochastic gradient descent (SGD) as the optimization algorithm. The concurrency level is set to Mc = 20, meaning that the server can aggregate results from up to 20 clients concurrently. The delay time for each client is sampled from a uniform distribution U(0, Tmax), where Tmax = 20 seconds by default. We run all experiments for a total of T = 600 global communication rounds. For FMNIST, we utilize a convolutional neural network (CNN) consisting of three convolutional layers and two fully connected layers. For CIFAR-10, we adopt a Res Net-18 architecture (He et al., 2016). |