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This paper proposes encoder-sharing hierarchical multi-task federated learning (EN-HMTFL), which integrates cluster-based hierarchical federated learning with a globally shared encoder and vehicle-local decoders.
A topology-coupled urgency~(TCU) scheduler that minimizes the FC's accumulated estimation error while incorporating the swarm's expected algebraic connectivity into the scheduling objective.
Most learning-based reconfigurable intelligent surface (RIS) designs assume continuous control or closed-box channel models, limiting physically grounded end-to-end (E2E) training and neglecting practical 1-bit hardware constraints. We propose a physics-informed framework for RIS-assisted MIMO–OFDM that embeds an optic...
An AutoEncoder-based Reliability-Optimized Hierarchical Multi-Task Federated Learning (AERO-HMTFL) framework for dynamic multi-hop clustered VANETs is proposed, which introduces a tri-weighted clustering metric that jointly considers vehicular mobility, shared-model similarity, and task affinity to produce mobility-sta...
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