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 [1].
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
Authors: Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we numerically study the role of personalization when the data distributions are heterogeneous. In particular, we consider the multi-class classification problem over MNIST [43] and CIFAR-10 [44] datasets and distribute the training data between n users as follows: ... The test accuracy results along with the 95% confidence intervals are reported in Table 1. |
| Researcher Affiliation | Academia | Alireza Fallah EECS Department Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL Aryan Mokhtari ECE Department University of Texas at Austin Austin, TX 78712 EMAIL Asuman Ozdaglar EECS Department Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL |
| Pseudocode | Yes | Algorithm 1: The proposed Personalized Fed Avg (Per-Fed Avg) Algorithm |
| Open Source Code | No | The paper states |
| Open Datasets | Yes | In particular, we consider the multi-class classification problem over MNIST [43] and CIFAR-10 [44] datasets |
| Dataset Splits | No | The paper mentions distributing training data and dividing test data, but it does not specify a distinct validation set or its split percentage for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | We use a neural network with two hidden layers with sizes 80 and 60, and we use Exponential Linear Unit (ELU) activation function. We take n = 50 users in the network, and run all three algorithms for K = 1000 rounds. At each round, we assume rn agents with r = 0.2 are chosen to run τ local updates. The batch sizes are D = D = 40 and the learning rate is β = 0.001. |