Federated Learning over Connected Modes
Authors: Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that FLOCO accelerates the global training process, and significantly improves the local accuracy with minimal computational overhead in cross-silo federated learning settings. Our experiments show that FLOCO outperforms common FL baselines (Fed Avg [1], Fed Prox [15]) and state-of-the-art personalized FL approaches (Fed Ro D [16], APFL [17], Ditto [18], Fed Per [19]) on both local and global test metrics without introducing significant computational overhead in cross-silo FL settings. |
| Researcher Affiliation | Academia | Dennis Grinwald1,2, Philipp Wiesner2, Shinichi Nakajima1,2,3 1BIFOLD, 2TU Berlin, 3RIKEN Center for AIP {dennis.grinwald, wiesner, nakajima}@tu-berlin.de |
| Pseudocode | Yes | Algorithm 1: Federated Learning over Connected Modes (FLOCO). Input :number of communication rounds T, number of clients K, simplex dimension M, subregion assignment round τ, subregion radius ρ |
| Open Source Code | Yes | Our code is publicly available: https://github.com/dennis-grinwald/floco. |
| Open Datasets | Yes | To evaluate our method, we perform image classification on the CIFAR10 [35] and FEMNIST [36] datasets. |
| Dataset Splits | No | The paper discusses training and testing data but does not explicitly mention a 'validation' set or split in the context of data partitioning. |
| Hardware Specification | No | Justification: We did not explicitly compute the resources needed. |
| Software Dependencies | No | The paper mentions FL frameworks FL-bench [20] and Flower [21] but does not specify their version numbers or other software dependencies with specific versions. |
| Experiment Setup | Yes | We provide a table with the training hyperparameters that we use for each dataset/model setting in Appendix B. For the baselines, we follow the recommended parameter settings by the authors, which are detailed in Appendix B. Table 6: Summary of used hyperparameters for training. Dataset/Model T K |St| e E/EDITTO γ mom. wd µ CIFAR-10/Cifar CNN 500 100 30 50 5 0.02 0.5 10 5 0.01 CIFAR-10/Res Net-18 100 100 10 32 5 0.01 0.9 10 4 0.01 FEMNIST/Femnist CNN 350 100 10 32 5 0.1 0.0 0.0 0.01 FEMNIST/Squeeze Net V1 1000 100 10 32 5 0.005 0.0 10 4 0.01 |