Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning
<|reference_start|>Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning: We propose an uplink over-the-air aggregation (OAA) method for wireless federated learning (FL) that simultaneously trains multiple models. To maximize the multi-model training convergence rate, we derive an upper bound on the optimality gap of the global model update, and then, formulate an uplink joint transmit-receive beamforming optimization problem to minimize this upper bound. We solve this problem using the block coordinate descent approach, which admits low-complexity closed-form updates. Simulation results show that our proposed multi-model FL with fast OAA substantially outperforms sequentially training multiple models under the conventional single-model approach.<|reference_end|>
@article{zhang2024uplink,
    title={Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated
  Learning},
    author={Chong Zhang and Min Dong and Ben Liang and Ali Afana and Yahia Ahmed},
    journal={arXiv preprint arXiv:2409.00978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00978},
    primaryClass={cs.IT eess.SP math.IT}
}