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A new ontology for decentralization reveals that existing definitions often lead to contradictory conclusions in decentralized AI systems.
Adversaries can exploit short-term wireless disruptions to not only take drones offline but also impersonate them, creating a dual threat to federated learning systems.
Federated learning not only meets privacy standards but also outperforms centralized models in ECG anomaly detection, revealing a new paradigm for secure health monitoring on edge devices.
