The RL-based weighted aggregation approach in FedStitch, while effective in addressing the challenges posed by non-IID (Independent and Identically Distributed) data distributions, faces several potential challenges and limitations. One significant challenge is the reliance on accurate estimation of the non-IID levels among clients. If the reinforcement learning model fails to correctly identify clients with lower non-IID data, it may inadvertently assign higher weights to clients whose data distributions are not representative of the overall task, leading to suboptimal block selection and degraded model performance.
Additionally, the exploration-exploitation trade-off inherent in the Epsilon-Greedy algorithm can introduce variability in the aggregation process. If the exploration rate is too high, the model may prioritize less relevant blocks, while a low exploration rate may lead to stagnation and prevent the discovery of potentially better-performing blocks. This balance is crucial, especially in scenarios with highly skewed data distributions, where certain classes may be underrepresented across clients.
Furthermore, the RL-based approach may require extensive training data to effectively learn the optimal weighting strategy, which could be a limitation in environments where data is scarce or highly imbalanced. The computational overhead associated with reinforcement learning can also be a concern, particularly in resource-constrained settings, as it may increase the time and energy costs of the aggregation process.