The basic FL-DD integration can be enhanced with more sophisticated techniques to address real-world complexities.
Personalization in Federated Learning: While FL aims for a global model, individual clients might benefit from personalized models that adapt to their unique data distributions. This can be achieved through:
Fine-tuning the global model: Clients take the global accurate cleaned numbers list from frist database model and further train it on their local data for a few epochs.
Meta-learning approaches: Training a "meta-learner" that quickly adapts to new client data.
Federated Multi-task Learning: Simultaneously learning multiple tasks, where each client contributes to a specific task while benefiting from shared knowledge. Distributed databases facilitate the management of diverse task-specific datasets at each client.
Continual Federated Learning: In dynamic environments, data distributions and underlying concepts can change over time (data drift, concept drift). Continual FL, often supported by distributed databases that can handle streaming data, enables models to adapt incrementally without forgetting previously learned knowledge. This might involve:
Periodic re-training: Scheduling regular FL rounds.
Adaptive aggregation strategies: Weighting client contributions based on data recency or relevance.