SCAFFOLD Implementation Details Within MTGC Repository Answering Key Questions

Hey guys,

I've got two questions about the SCAFFOLD implementation within the MTGC repository, and I'm hoping someone can help me out. I really appreciate you releasing the MTGC code – it's awesome!

Running SCAFFOLD

So, first off, I'm curious about the specifics of running SCAFFOLD for the comparison in the paper. Specifically, I'm trying to figure out the exact script and parameter settings used. Did you, for example, invoke run_MTGC_Z.py with N=1 and E=1? This should effectively reduce MTGC to SCAFFOLD, as described in Section 3.3 of the paper, but I want to make sure I'm doing it right.

To elaborate further on this, understanding the nuances of parameter settings is crucial for replicating the results and conducting further experiments. The interplay between the number of clients (N) and the number of local epochs (E) can significantly impact the performance and convergence of the algorithm. When N is set to 1, we are essentially dealing with a single client scenario, which simplifies the federated learning setup. Setting E to 1 means that each client performs only one local epoch of training before the model updates are aggregated. This configuration is key to mimicking the behavior of SCAFFOLD within the MTGC framework.

However, other parameters might also play a role. For instance, the learning rate, the choice of optimizer, and the batch size can all influence the training process. It would be helpful to know the specific values used for these parameters in the SCAFFOLD experiments. Furthermore, understanding the initialization of the model and the control variates is essential for ensuring a fair comparison. Did you use the same initialization strategy for both MTGC and SCAFFOLD? How were the control variates initialized in the SCAFFOLD setting? These details are critical for reproducing the results and gaining a deeper understanding of the algorithm's behavior.

Moreover, the data partitioning strategy can also affect the performance. Was the data split evenly across clients, or was there any form of non-IID (non-independent and identically distributed) data distribution? Understanding the data distribution is crucial for evaluating the robustness of the algorithm in real-world scenarios. Non-IID data distributions can pose significant challenges for federated learning algorithms, and it's important to know how SCAFFOLD and MTGC perform under such conditions. Therefore, knowing the specifics of the data partitioning strategy used in the experiments is vital for a comprehensive understanding. In summary, a detailed explanation of the experimental setup, including parameter settings, initialization strategies, and data partitioning, would greatly enhance the reproducibility and understanding of the results.

Standalone SCAFFOLD Code

My second question is about standalone SCAFFOLD code. Is there a separate, independent implementation of SCAFFOLD available? If there is, could you point me to the code or perhaps the relevant commit/branch within this repository? Having a standalone version would be super helpful for isolating and experimenting with SCAFFOLD independently of the MTGC framework. This can be particularly useful for debugging, profiling, and understanding the core mechanics of the algorithm. A standalone implementation often strips away the complexities of a larger framework, making it easier to focus on the specific functionalities of SCAFFOLD.

Furthermore, a standalone version could facilitate easier integration into other systems or frameworks. Researchers and practitioners might want to use SCAFFOLD as a building block in their own federated learning pipelines, and a standalone implementation would greatly simplify this process. It would also allow for easier comparison with other federated learning algorithms. By having a clean and self-contained version of SCAFFOLD, it becomes much simpler to benchmark its performance against other approaches and identify its strengths and weaknesses. This kind of comparative analysis is crucial for advancing the field of federated learning.

In addition, a separate SCAFFOLD codebase could serve as an educational resource. Students and newcomers to the field could use it to learn the intricacies of the algorithm without being overwhelmed by the complexity of a larger system. The simplicity of a standalone implementation can make the learning process much more accessible and effective. By providing a clear and concise codebase, you would be contributing to the broader understanding and adoption of SCAFFOLD. This, in turn, could lead to further research and development in the area of federated learning. Therefore, sharing a standalone SCAFFOLD implementation, if available, would be a valuable contribution to the community, fostering both practical applications and academic research.

Thanks again for your time and effort in releasing this code! I'm really looking forward to hearing back from you.