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..
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
Authors: Mengmeng Ma, Tang Li, Xi Peng
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on a variety of real-world datasets verifies TFL s superior OOF robustness and scalability. ... 4. Experiments ... 4.2. Evaluation on OOF-resiliency ... 4.3. Evaluation on Scalability ... 4.4. Evaluation on In-federation Performance ... 4.5. Evaluation on Effectiveness of Client Clustering ... 4.6. Ablation Study |
| Researcher Affiliation | Academia | Mengmeng Ma 1 Tang Li 1 Xi Peng 1 Deep REAL Lab, Department of Computer & Information Sciences, University of Delaware. |
| Pseudocode | Yes | Algorithm 1 Topology-aware Federated Learning |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | TFL is evaluated on our curated real-world datasets (①e ICU, ②Fe TS, ③TPT-48) and standard benchmarks (④CIFAR-10/-100, ⑤PACS), spanning a wide range of tasks including classification, regression, and segmentation. ... ①e ICU (Pollard et al., 2018) ... ②Fe TS (Pati et al., 2022b) ... ③TPT-48 (Vose et al., 2014) ... ④CIFAR-10/-100 (Krizhevsky & Hinton, 2009) ... ⑤PACS (Li et al., 2017) |
| Dataset Splits | No | The paper describes training and testing splits (e.g., '3 domains are used (15 clients) for training and 1 domain (5 clients) for testing' for PACS), but does not explicitly provide details about a separate validation set or its split percentage/counts. |
| Hardware Specification | Yes | All experiments are conducted using a server with 8 NVIDIA A6000 GPUs. |
| Software Dependencies | No | The paper mentions optimizers (SGD) and model architectures (ResNet18, U-Net) but does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA 11.x). |
| Experiment Setup | Yes | For the PACS dataset... learning rate of 0.01, momentum of 0.9, weight decay of 5e 4, and a batch size of 8. ... For e ICU... 30 communication rounds, using a batch size of 64 and a learning rate of 0.01, and report the performance on unseen hospitals. Within each communication round, clients perform 5 epochs (E = 5) of local optimization using SGD. |