
## FedNL: Making Newton-Type Methods Applicable to Federated Learning
Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtarik
Keywords: 
ICML/2022/Proceedings/17083 - FedNL: Making Newton-Type Methods Applicable to Federated Learning.pdf
Project URL: nan

### 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 provide their implementation. Pseudo code is available in algorithm 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?_

[3]

(5/5)

The authors take datasets from a public library which they cite. In appendix B.1. they provide statistics on the data set.

### 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?_

[3]

The parameter settings are discussed in sec 5.1. and appendix B.2. One parameter is experimented on a grid. No other acquisition specified.

### 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?_

[4]

The authors evaluate some function which is not very clear and perhaps requires field expertise to understand. There is no mention of training/test splitting. Results are single runs.

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

[5]

Requries expertise with federated learning and and second order methods.
