Editing Applications of Edge Computing (section)

===8.4.2 Federated Learning===
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Federated Learning is an ML technique that allows collaborative AI model training across distributed AUV swarms without centralized data collection—a huge advantage for underwater environments where connectivity is limited and data privacy is essential. 

Federated Learning systems have a three-tier learning architecture.
* '''Device Level''': This is where local model training and energy-constrained optimization occurs. This level also implements some differential (adding noise to model updates to prevent data leakage) privacy mechanisms to protect data.
* '''Edge Aggregation Level''': This level combines model updates from multiple AUVs using federated averaging (FedAvg). It also flags suspicious model updates (eg, from malfunctioning AUVs).
* '''Global Updates''': This level retrains the global model with edge-aggregated data and marine scientists validate models before redeployment. Additionally, it implements Long-Term Learning where monthly retraining cycles incorporate new oceanographic data.

Federated Learning techniques are known to save energy to upto 42% when compared to centralized training, with 15-20% faster convergence speed and a lower accuracy loss.

'''Advantages'''
* Privacy Preservation: Raw data never leaves AUVs (crucial for military/ecological missions).
* Bandwidth Efficiency: Only model updates (not datasets) are transmitted.
* Adaptability: Models improve as more AUVs contribute.