text: Title: Add Flower Baseline: [FedRS]
  Body: ### Paper
  
  Xin-Chun Li, De-Chuan Zhan. FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data (KDD'21)
  
  ### Link
  
  https://dl.acm.org/doi/10.1145/3447548.3467254
  
  ### Maybe give motivations about why the paper should be implemented as a baseline.
  
  FedRS (Federated Learning with Restricted Softmax) is a method to correct the negative effect of local training with missing classes by restricting the update of weights via a corrected softmax term. The main parameter is alpha (between 0.0 and 1.0), which determines the strength of this correction. If alpha=1.0, this is same as vanilla FedAvg. 
  
  FedRS (100+ citations) is referenced in other papers exploring Federated Learning with non-iid datasets, such as [FedLC](https://arxiv.org/abs/2209.00189) (Federated Learning with Label Distribution Skew via Logits Calibration) and [FedConcat](https://arxiv.org/abs/2312.06290) (Exploiting Label Skews in Federated Learning with Model Concatenation). 
  
  The plan is to reproduce the results for FedAvg and FedRS (with different alpha values) in Table 5 of the paper:
  <img width="509" alt="Screenshot 2024-11-19 at 06 14 23" src="https://github.com/user-attachments/assets/66f11575-5c62-4c9d-b501-c9f368391c33">
  
  ### Is there something else you want to add?
  
  I've found no mention of FedRS in PRs or in existing baselines. I've started the repro process.
  
  ### Implementation
  
  #### To implement this baseline, it is recommended to do the following items in that order:
  
  ### For first time contributors
  
  - [x]  Read the [`first contribution` doc](https://flower.ai/docs/first-time-contributors.html)
  - [x]  Complete the Flower tutorial
  - [x]  Read the Flower Baselines docs to get an overview:
      - [x]  [How to use Flower Baselines](https://flower.ai/docs/baselines/how-to-use-baselines.html)
      - [x]  [How to contribute a Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)
  
  ### Prepare - understand the scope
  
  - [X] Read the paper linked above
  - [X] Decide which experiments you'd like to reproduce. The more the better!
  - [X] Follow the steps outlined in [Add a new Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html#add-a-new-flower-baseline).
  - [X] You can use as reference [other baselines](https://github.com/adap/flower/tree/main/baselines) that the community merged following those steps.
  
  ### Verify your implementation
  
  - [X] Follow the steps indicated in the `EXTENDED_README.md` that was created in your baseline directory
  - [X] Ensure your code reproduces the results for the experiments you chose
  - [X] Ensure your `README.md` is ready to be run by someone that is no familiar with your code. Are all step-by-step instructions clear?
  - [X] Ensure running the formatting and typing tests for your baseline runs without errors.
  - [X] Clone your repo on a new directory, follow the guide on your own `README.md` and verify everything runs.