Personalized Online Federated Learning with Multiple Kernels
Authors: Pouya M. Ghari, Yanning Shen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 pmollaeb@uci.edu Yanning Shen University of California Irvine yannings@uci.edu |
| 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. |