<ul><li>Leveraging collective compute power of edge devices for distributed learning is gaining interest in wireless networks.</li><li>Critical challenges exist in optimizing learning at the network edge, including the trade-off between predictive accuracy and interpretability.</li><li>Integrating inherently explainable models like decision trees in distributed learning is difficult due to their non-differentiable structure.</li><li>Combining continual learning strategies with federated learning supports adaptive, lifelong learning in resource-limited environments.</li></ul>

Explainability and Continual Learning meet Federated Learning at the Network Edge

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