TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
Authors: Mengdi Wang, Anna Bodonhelyi, Efe Bozkir, Enkelejda Kasneci
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Turbo SVM-FL on multiple datasets including FEMNIST, Celeb A, and Shakespeare using user-independent validation with non-iid data distribution. Our results show that Turbo SVM-FL can significantly outperform existing popular algorithms on convergence rate and reduce communication rounds while delivering better test metrics including accuracy, F1 score, and MCC. |
| Researcher Affiliation | Academia | Chair for Human-Centered Technologies for Learning, Technical University of Munich, Munich, Bavaria, Germany {mengdi.wang, anna.bodonhelyi, efe.bozkir, enkelejda.kasneci}@tum.de |
| Pseudocode | Yes | A pseudocode for Turbo SVM-FL is given in Algorithm 3, and a graphical illustration is depicted Figure 1. Algorithm 1: Turbo SVM-FL part 1: selective aggregation. Algorithm 2: Turbo SVM-FL part 2: max-margin spread-out regularization. Algorithm 3: The Turbo SVM-FL Framework. |
| Open Source Code | Yes | For more details such as reproducibility and model structures, we redirect readers to the Appendix and our Git Hub repository1. 1 https://github.com/Kasneci-Lab/Turbo SVM-FL. |
| Open Datasets | Yes | We benchmarked Turbo SVM-FL on three different datasets covering data types of both image and nature language, namely FEMNIST (Le Cun 1998; Cohen et al. 2017), Celeb A (Liu et al. 2015), and Shakespeare (Shakespeare 2014; Mc Mahan et al. 2017) (Table 1). All three datasets can be acquired on LEAF (Caldas et al. 2018). |
| Dataset Splits | Yes | More specifically, we conducted 90% 10% train-test-split in a user-independent way, which means we had a held-out set of clients for validation rather than a fraction of validation data on each client (Wang et al. 2021a). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Algorithm 3: The Turbo SVM-FL Framework lists specific inputs/hyperparameters: clients n [N], client local datasets D1, ..., DN, |DG| = |D1| + ... + |DN|, number of global epochs T, number of client epochs E, number of classes K, server learning rate ηG, client learning rate η, mini-batch size B. |