HEART schedules multiple federated learning tasks across vehicles and edge servers using hybrid synchronous-asynchronous aggregation and a two-stage PSO-GA plus greedy optimizer, reducing total training time in simulations.
HFL-TranWGAN: Knowledge-Driven Cross-Domain Collaborative Anomaly Detec- tion for End-to-End Network Slicing,
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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning
HEART schedules multiple federated learning tasks across vehicles and edge servers using hybrid synchronous-asynchronous aggregation and a two-stage PSO-GA plus greedy optimizer, reducing total training time in simulations.