Multi-Session Budget Optimization for Forward Auction-based Federated Learning
Authors: Xiaoli Tang, Han Yu, Zengxiang Li, Xiaoxiao Li
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six benchmark datasets show that it significantly outperforms seven state-of-the-art approaches. On average, MBOS-AFL achieves 12.28% higher utility, 14.52% more data acquired through auctions for a given budget, and 1.23% higher test accuracy achieved by the resulting FL model compared to the best baseline. |
| Researcher Affiliation | Academia | 1College of Computing and Data Science, Nanyang Technological University, Singapore 2Department of Electrical and Computer Engineering, The University of British Columbia, Canada 3Vector Institute, Canada. Correspondence to: Han Yu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 The training procedure of MBOS-AFL |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | The performance assessment of MBOS-AFL is conducted on the following six widely-adopted datasets in FL studies: 1) MNIST3, 2) CIFAR-104, 3) Fashion MNIST (i.e., FMNIST) (Xiao et al., 2017), 4) EMNISTdigits (i.e., EMNISTD), 5) EMNIST-letters (i.e., EMNISTL) (Cohen et al., 2017) and 6) Kuzushiji-MNIST (i.e., KMNIST) (Clanuwat et al., 2018). 3http://yann.lecun.com/exdb/mnist/ 4https://www.cs.toronto.edu/kriz/cifar.html |
| Dataset Splits | Yes | Experiment Scenarios: We compare MBOS-AFL with baselines under two main experiment scenarios with each containing 10,000 DOs: 1) IID data, varying dataset sizes, without noise: In this scenario, the sizes of datasets owned by various DOs are randomly generated, ranging from 500 to 5,000 samples. [...] 2) Non IID data, with noise: In this experimental scenario, we deliberately introduce data heterogeneity by adjusting the class distribution among individual DOs. Following (Tang et al., 2024b), we implement the following Non-IID setup. [...] In this experiment scenario, each DO holds 3,000 images. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using Deep Q-Network (DQN) technique and neural networks, and RMSprop for training, but does not provide specific version numbers for any programming languages, libraries, or frameworks (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | The replay buffer D of both the Inter BPA and the Intra BMA are set to 5,000. During training, both agents explore the environment using an ϵ-greedy policy with an annealing rate from 1.0 to 0.05. In updating both Qintra and Qinter, 64 tuples uniformly sampled from D are used for each training step, and the corresponding target networks are updated once every 20 steps. In our experiments, we use RMSprop with a learning rate of 0.0005 to train all neural networks, and set the discount factor γ to 1. In addition, we have set the number of candidate DOs within each session to 200 (i.e., Cs = 200). The communication round in each session is set at 100, while the local training epoch is set at 30. |