Description: The learning objective is the purpose that a machine learning algorithm seeks to achieve during its training process. This objective is defined through a reward or loss function, which guides the model in its learning. In reinforcement learning, the objective focuses on maximizing the accumulated reward over time, meaning the algorithm must learn to make optimal decisions in a dynamic environment. On the other hand, in federated learning, the goal is to train a model collaboratively without sharing sensitive data, allowing algorithms to learn from multiple sources while preserving data privacy. Both approaches require a clear definition of their learning objectives to ensure that the model is effectively trained and meets established expectations. The precise formulation of the learning objective is crucial, as it influences the model architecture, the optimization algorithms used, and ultimately the system’s performance. In summary, the learning objective acts as a compass that guides the training process, ensuring that the model evolves toward a desired state, whether maximizing rewards or minimizing prediction errors.
