A julia framework for personalized federated learning with mechanism models. In examples/Cifar10.jl, examples/Covtype.jl and so on, a main function is implemented. The main function takes 4 arguments: \lambda (trust level of P-KM), p (ratio of data to use), withMech (whether to use knowledge model) and withFed (which federeted learning training algorithm) , e.g., main(0.3, 0.01, true, 1). The possible combination includes:
| withMech | withFed | Method name |
|---|---|---|
| false | 0 | ML |
| true | 0 | MLwKM |
| false | 1 | FL |
| true | 1 | FLwKM |
| false | 2 | ADAP |
| true | 2 | ADAPwKM |
| false | 3 | DITTO |
| true | 3 | DITTOwKM |
Steps to run the experiments
- Open Julia
include("examples/Cifar10.jl")- Then call the main function, e.g.,
main(0.3, 0.01, true, 1).
Steps to run the experiments
- Download correspoding dataset to data/ folder.
- When calling the main function, add additional argument
dataPath, e.g.,main(0.3, 0.01, true, 1; dataPath="data/covtype")
Steps to run the experiments
- Download YearPredictionMSD dataset and put to data/YearPredictionMSD
- The other steps are similar to above