
## Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
Keywords: Machine Learning: Ensemble Methods, Knowledge Representation and Reasoning: Applications
IJCAI/2022/Proceedings/0399 - Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning.pdf

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[10]

The authors do not share their implementation. Pseudo code is provided in algorithm 1. An overview is given in figure 1.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[5]

(3/3)

The authors use three datasets, all of which are public. They only state how the data is augmented before the method is applied. No citations, descriptions or statistics given. No direct links found.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

The method seems to only have semantic parameters, the values of which are specified in section 4.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[7]

The authors do not specify how the test set is acquired. The metric is accuracy. They vary the alpha parameter in the experiments. The variance is not clear.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[8]

Requires experience with ensemble, transfer and federated learning.
