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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Personalized Online Federated Learning with Multiple Kernels
Authors: Pouya M. Ghari, Yanning Shen
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real datasets showcase the advantages of the proposed algorithm compared with other online federated kernel learning ones. |
| Researcher Affiliation | Academia | Pouya M. Ghari University of California Irvine EMAIL Yanning Shen University of California Irvine EMAIL |
| Pseudocode | Yes | Algorithm 1 The k-th client kernel subset selection at time t. and Algorithm 2 Personalized Online Federated Multi-Kernel Learning (POF-MKL) |
| Open Source Code | Yes | Codes are available at https://github.com/pouyamghari/POF-MKL. |
| Open Datasets | Yes | real datasets downloaded from UCI machine learning repository [11]: Naval [7], UJI [47], Air [51] and WEC [35]. |
| Dataset Splits | No | The paper describes data distribution among clients (i.i.d vs non-i.i.d) and total samples (T=500), but does not explicitly provide percentages or counts for training, validation, or test splits. |
| Hardware Specification | Yes | all experiments were carried out using Intel(R) Core(TM) i7-10510U CPU @ 1.80 GHz 2.30 GHz processor with a 64-bit Windows operating system. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Learning rates are set to η = ηk = 1/T, k. Also, exploration rates are set to ξk = 1, k. The dictionary of kernels consists of 51 RBFs with different bandwidth such that the bandwidth of the i-th kernel is σi = 10 2i 52 / 25. The maximum value can be picked for the number of random features D is 100. The maximum number of parameters that a client is allowed to transmit to the server is 1000. |