PeFLL: Personalized Federated Learning by Learning to Learn
Authors: Jonathan Scott, Hossein Zakerinia, Christoph H Lampert
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we report on our experimental evaluation. The values reported in every table and plot are given as the mean together with the standard deviation across three random seeds. |
| Researcher Affiliation | Academia | Jonathan Scott Institute of Science and Technology Austria (ISTA) jonathan.scott@ist.ac.at Hossein Zakerinia Institute of Science and Technology Austria (ISTA) hossein.zakerinia@ist.ac.at Christoph H. Lampert Institute of Science and Technology Austria (ISTA) chl@ist.ac.at |
| Pseudocode | Yes | Pseudocode of the specific steps is provided in Algorithms 1 and 2. |
| Open Source Code | Yes | We provide the code as supplemental material. We will publish it when the anonymity requirement is lifted. |
| Open Datasets | Yes | For our experiments, we use three datasets that are standard benchmarks for FL: CIFAR10/CIFAR100 (Krizhevsky, 2009) and FEMNIST (Caldas et al., 2018). [...] Additional experiments on the Shakespeare dataset (Caldas et al., 2018) are provided in Appendix A. |
| Dataset Splits | Yes | The hyperparameters for all methods are tuned using validation data that was held out from the training set (10,000 samples for CIFAR10 and CIFAR100, spread across the clients, and 10% of each client s data for FEMNIST). |
| Hardware Specification | No | The paper mentions support from 'Scientific Computing (Sci Comp)' and that a ResNet20 implementation was used, but does not provide specific details on the CPU, GPU, or memory used for experiments. |
| Software Dependencies | No | The paper mentions the use of 'SGD' as the optimizer and implies the use of a deep learning framework (e.g., PyTorch for ResNet implementation) but does not specify exact version numbers for any software dependencies. |
| Experiment Setup | Yes | We train all methods, except Local, for 5000 rounds with partial client participation. For CIFAR10 and CIFAR100 client participation is set to 5% per round... The optimizer used for training at the client is SGD with a batch size of 32, a learning rate chosen via grid search and momentum set to 0.9. The batch size used for computing the descriptor is also 32. [...] the dimension of the embedding vectors is l = n/4 and the number of client SGD steps is k = 50. The regularization parameters for the embedding network and hypernetwork are set to λh = λv = 10-3, while the output regularization is λθ = 0. |