Federated Reconstruction: Partially Local Federated Learning
Authors: Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, Sushant Prakash
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We next describe experiments validating FEDRECON on matrix factorization and next word prediction. We aim to determine whether reconstruction can enable practical partially local federated learning with fast personalization for new clients, including in settings without user-specific embeddings. |
| Researcher Affiliation | Industry | Karan Singhal Google Research EMAIL Hakim Sidahmed Google Research EMAIL Zachary Garrett Google Research EMAIL Shanshan Wu Google Research EMAIL Keith Rush Google Research EMAIL Sushant Prakash Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Federated Reconstruction Training |
| Open Source Code | Yes | Release an open-source library for evaluating algorithms across tasks in this setting.1 1https://git.io/federated_reconstruction |
| Open Datasets | Yes | We evaluate on federated matrix factorization using the popular Movie Lens 1M collaborative filtering dataset [29]. We perform next word prediction with the federated Stack Overflow dataset introduced in Tensor Flow [51]. |
| Dataset Splits | Yes | We split each user s ratings into 80% train, 10% validation, and 10% test by timestamp. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow Federated library' and 'Keras model' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We train for 1500 rounds using a total of 100 clients per round, 10 epochs of local training, and batch size 32 for each client. For FEDRECON, we use 10 local reconstruction steps and 1 local global update step. Additional details on hyperparameter choices for each task are provided in Appendix C. |