Building generalized models is crucial, but overfitting for specific cases—like unique weather patterns or reflections in security camera footage—can enhance accuracy. Federated learning presents challenges in training models across distributed data locations, requiring innovative solutions to manage network constraints and optimize the training process without overwhelming latency.
