NeFL can be extended to address more complex forms of system and statistical heterogeneity by incorporating adaptive mechanisms that respond to real-time changes in client capabilities and data distributions. For dynamic resource constraints, NeFL could implement a feedback loop where clients continuously report their resource availability (e.g., CPU, memory, bandwidth) to the server. This information could be used to dynamically adjust the scaling of submodels, allowing clients to switch between different submodels based on their current resource status. Additionally, integrating reinforcement learning techniques could enable the system to learn optimal submodel configurations over time, adapting to fluctuations in client resources.
To handle non-IID data distributions that change over time, NeFL could incorporate a mechanism for continuous learning. This could involve periodically reassessing the data distribution across clients and adjusting the training process accordingly. For instance, clients could be grouped based on their data characteristics, and the server could employ a more sophisticated aggregation method that accounts for the evolving nature of the data. Techniques such as clustering clients based on their data distributions or using meta-learning approaches to adapt the model to new data distributions could enhance NeFL's robustness against statistical heterogeneity.