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..
FedAvP: Augment Local Data via Shared Policy in Federated Learning
Authors: Minui Hong, Junhyeog Yun, Insu Jeon, Gunhee Kim
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, our Fed Av P demonstrates superior performance on CIFAR-10/100 [21], SVHN [22], and FEMNIST [23] datasets within an FL context, compared to existing federated learning algorithms, including Fed Avg [2], Fed Prox [4], Fed Dyn [5], Fed Ex P [6], and federated data augmentation algorithms, including Fed Gen [9], Fed Mix [8], and Fed FA [11]. |
| Researcher Affiliation | Academia | Minui Hong Junhyeog Yun Insu Jeon Gunhee Kim Seoul National University, Seoul, South Korea EMAIL {insuj3on}@gmail.com |
| Pseudocode | Yes | Algorithm 1 Fed Av P: Joint Training |
| Open Source Code | Yes | Our code is available at https://github.com/alsdml/Fed Av P. |
| Open Datasets | Yes | In the experiments, our Fed Av P demonstrates superior performance on CIFAR-10/100 [21], SVHN [22], and FEMNIST [23] datasets within an FL context... |
| Dataset Splits | Yes | We assign the data to 130 clients based on a Dirichlet distribution with different hyperparameters of α = [5.0, 0.1], as done in p FLBench [25]. The smaller α is, the higher the degree of heterogeneity is. Among these clients, only 100 randomly selected clients participate in the training, while the remaining 30 are nominated as out-of-distribution (OOD) clients. |
| Hardware Specification | Yes | All experiments are run on a cluster of 32 NVIDIA GTX 1080 GPUs. |
| Software Dependencies | No | We utilized both the Tree-structured Parzen Estimator algorithm and Random Sampler as hyperparameter samplers within Optuna. We leveraged a Py Torch implementation [47]. We used the Adam optimizer [49]. |
| Experiment Setup | Yes | For the Fed Av P algorithm, the hyperparameters include the server policy learning rate η, client policy learning rate λ, gradient clipping threshold c, and a regularization term ϵ. In our experimentation, we tuned η within [0.4, 0.9], λ within [0.1 0.9], c within [0.4 1.0], and ϵ within [0.0 0.5]. The validation batch size was also explored within [64, 128, 192]. A common hyperparameter across all methods was local epoch set to 5, and local batch is set to 64. The client model learning rate γ was searched within the range of [0.1 0.3]. |