Personalized Federated Learning With Gaussian Processes
Authors: Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on standard PFL benchmarks with CIFAR-10, CIFAR-100, and CINIC-10, and on a new setup of learning under input noise show that p Fed GP achieves well-calibrated predictions while significantly outperforming baseline methods, reaching up to 21% in accuracy gain. 6 Experiments We evaluated p Fed GP against baseline methods in various learning setups. |
| Researcher Affiliation | Collaboration | Idan Achituve Bar-Ilan University, Israel idan.achituve@biu.ac.il Aviv Shamsian Bar-Ilan University, Israel aviv.shamsian@biu.ac.il Aviv Navon Bar-Ilan University, Israel aviv.navon@biu.ac.il Gal Chechik Bar-Ilan University, Israel NVIDIA, Isreal gal.chechik@biu.ac.il Ethan Fetaya Bar-Ilan University, Israel ethan.fetaya@biu.ac.il |
| Pseudocode | Yes | Algorithm 1 p Fed GP. C clients indexed by c; E number of local epochs; |S| number of sampled clients; M number of inducing inputs per class |
| Open Source Code | Yes | Our code is publicly available at https://github.com/Idan Achituve/p Fed GP |
| Open Datasets | Yes | All methods were evaluated on CIFAR-10, CIFAR-100 [38], and CINIC-10 [15] datasets. |
| Dataset Splits | Yes | We tuned the hyperparameters of all methods using a pre-allocated held-out validation set. |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify versions for software dependencies or libraries (e.g., Python version, PyTorch/TensorFlow version, specific library versions). |
| Experiment Setup | Yes | We limit the training process to 1000 communication rounds, in each we sample five clients uniformly at random for model updates. In the local model, we performed 100 epochs of training for each client. In all experiments, we used a Le Net-based network [40] having two convolution layers followed by two fully connected layers and an additional linear layer. |