The flexibility offered by NeFL's submodel scaling comes with several potential trade-offs. One significant trade-off is the increased communication overhead. In a traditional federated learning setup with a single global model, clients send updates for a uniform model, which simplifies the aggregation process. However, with NeFL, clients may need to communicate updates for multiple submodels, each with different architectures and parameters. This can lead to increased bandwidth usage and longer communication times, especially if clients frequently switch between submodels.
Another trade-off is the complexity of model management. NeFL's approach of handling inconsistent parameters and multiple submodels introduces additional complexity in both the training and aggregation processes. The server must manage the aggregation of parameters from various submodels, which requires more sophisticated algorithms and potentially more computational resources. This complexity could lead to challenges in ensuring consistency and convergence across the different submodels, particularly in scenarios with high client variability.
In contrast, a single global model simplifies the training and aggregation process but may not effectively utilize the diverse capabilities of heterogeneous clients. Therefore, while NeFL provides greater flexibility and adaptability to client needs, it also necessitates careful consideration of communication efficiency and model management complexity.