
## Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection
Wei Wan, Shengshan Hu, jianrong Lu, Leo Yu Zhang, Hai Jin, Yuanyuan He
Keywords: AI Ethics, Trust, Fairness: Trustworthy AI, Machine Learning: Adversarial Machine Learning
IJCAI/2022/Proceedings/0106 - Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection.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?_

[9]

The authors do not publish their implementation. The authors share five pieces of pseudo code and details their process in figure 2. No practical details shared.

### 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]

(2/2)

The authors evaluate the method on two public datasets. They are not cited, linked, described or statistics given.

### 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 authors state the architectures used for the NNs employed in their method. They state the input parameters of their method in algorithm 2 with 4 configurable parameters. These values are stated in in section 5 under 'Evaluation Metrics and Parameter Settings'. It is not specified how these values were determined.

### 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?_

[2]

The authors state their evaluation metric (accuracy) and describe how it is calculated over their task. They state the training set selection in 5.1. but the test set is unclear (and whether this is applicable). 

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

[6]

Requires experience with robustness and federated learning. 
